HARP: a database of structural impacts of systematic missense mutations in drug targets of Mycobacterium leprae

Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges

Missense mutations are known contributors to diverse genetic disorders, due to their subtle, single amino acid changes imparted on the resultant protein. Because of this, understanding the impact of these mutations on protein stability and function is crucial for unravelling disease mechanisms and developing targeted therapies. The Critical Assessment of Genome Interpretation (CAGI) provides a valuable platform for benchmarking state-of-the-art computational methods in predicting the impact of disease-related mutations on protein thermodynamics. Here we report the performance of our comprehensive platform of structure-based computational approaches to evaluate mutations impacting protein structure and function on 3 challenges from CAGI6: Calmodulin, MAPK1 and MAPK3. Our stability predictors have achieved correlations of up to 0.74 and AUCs of 1 when predicting changes in ΔΔG for MAPK1 and MAPK3, respectively, and AUC of up to 0.75 in the Calmodulin challenge. Overall, our study highlights the importance of structure-based approaches in understanding the effects of missense mutations on protein thermodynamics. The results obtained from the CAGI6 challenges contribute to the ongoing efforts to enhance our understanding of disease mechanisms and facilitate the development of personalised medicine approaches.

Unlocking Opportunities for Mycobacterium leprae and Mycobacterium ulcerans

In the recent decade, scientific communities have toiled to tackle the emerging burden of drug-resistant tuberculosis (DR-TB) and rapidly growing opportunistic nontuberculous mycobacteria (NTM). Among these, two neglected mycobacteria species of the Acinetobacter family, Mycobacterium leprae and Mycobacterium ulcerans, are the etiological agents of leprosy and Buruli ulcer infections, respectively, and fall under the broad umbrella of neglected tropical diseases (NTDs). Unfortunately, lackluster drug discovery efforts have been made against these pathogenic bacteria in the recent decade, resulting in the discovery of only a few countable hits and majorly repurposing anti-TB drug candidates such as telacebec (Q203), P218, and TB47 for current therapeutic interventions. Major ignorance in drug candidate identification might aggravate the dramatic consequences of rapidly spreading mycobacterial NTDs in the coming days. Therefore, this Review focuses on an up-to-date account of drug discovery efforts targeting selected druggable targets from both bacilli, including the accompanying challenges that have been identified and are responsible for the slow drug discovery. Furthermore, a succinct discussion of the all-new possibilities that could be alternative solutions to mitigate the neglected mycobacterial NTD burden and subsequently accelerate the drug discovery effort is also included. We anticipate that the state-of-the-art strategies discussed here may attract major attention from the scientific community to navigate and expand the roadmap for the discovery of next-generation therapeutics against these NTDs.

Characterizing and predicting ccRCC-causing missense mutations in Von Hippel-Lindau disease

BACKGROUND

Mutations within the Von Hippel-Lindau (VHL) tumor suppressor gene are known to cause VHL disease, which is characterized by the formation of cysts and tumors in multiple organs of the body, particularly clear cell renal cell carcinoma (ccRCC). A major challenge in clinical practice is determining tumor risk from a given mutation in the VHL gene. Previous efforts have been hindered by limited available clinical data and technological constraints.

METHODS

To overcome this, we initially manually curated the largest set of clinically validated VHL mutations to date, enabling a robust assessment of existing predictive tools on an independent test set. Additionally, we comprehensively characterized the effects of mutations within VHL using in silico biophysical tools describing changes in protein stability, dynamics and affinity to binding partners to provide insights into the structure-phenotype relationship. These descriptive properties were used as molecular features for the construction of a machine learning model, designed to predict the risk of ccRCC development as a result of a VHL missense mutation.

RESULTS

Analysis of our model showed an accuracy of 0.81 in the identification of ccRCC-causing missense mutations, and a Matthew's Correlation Coefficient of 0.44 on a non-redundant blind test, a significant improvement in comparison to the previous available approaches.

CONCLUSION

This work highlights the power of using protein 3D structure to fully explore the range of molecular and functional consequences of genomic variants. We believe this optimized model will better enable its clinical implementation and assist guiding patient risk stratification and management.

AI-Driven Enhancements in Drug Screening and Optimization

The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline.

Identifying Innate Resistance Hotspots for SARS-CoV-2 Antivirals Using In Silico Protein Techniques

The development and approval of antivirals against SARS-CoV-2 has further equipped clinicians with treatment strategies against the COVID-19 pandemic, reducing deaths post-infection. Extensive clinical use of antivirals, however, can impart additional selective pressure, leading to the emergence of antiviral resistance. While we have previously characterized possible effects of circulating SARS-CoV-2 missense mutations on proteome function and stability, their direct effects on the novel antivirals remains unexplored. To address this, we have computationally calculated the consequences of mutations in the antiviral targets: RNA-dependent RNA polymerase and main protease, on target stability and interactions with their antiviral, nucleic acids, and other proteins. By analyzing circulating variants prior to antiviral approval, this work highlighted the inherent resistance potential of different genome regions. Namely, within the main protease binding site, missense mutations imparted a lower fitness cost, while the opposite was noted for the RNA-dependent RNA polymerase binding site. This suggests that resistance to nirmatrelvir/ritonavir combination treatment is more likely to occur and proliferate than that to molnupiravir. These insights are crucial both clinically in drug stewardship, and preclinically in the identification of less mutable targets for novel therapeutic design.

Bioinformatics toolbox for exploring target mutation-induced drug resistance

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

Using Graph-Based Signatures to Guide Rational Antibody Engineering

Antibodies are essential experimental and diagnostic tools and as biotherapeutics have significantly advanced our ability to treat a range of diseases. With recent innovations in computational tools to guide protein engineering, we can now rationally design better antibodies with improved efficacy, stability, and pharmacokinetics. Here, we describe the use of the mCSM web-based in silico suite, which uses graph-based signatures to rapidly identify the structural and functional consequences of mutations, to guide rational antibody engineering to improve stability, affinity, and specificity.

Drug Resistance (Dapsone, Rifampicin, Ofloxacin) and Resistance-Related Gene Mutation Features in Leprosy Patients: A Systematic Review and Meta-Analysis

Dapsone (DDS), Rifampicin (RIF) and Ofloxacin (OFL) are drugs recommended by the World Health Organization (WHO) for the treatment of leprosy. In the context of leprosy, resistance to these drugs occurs mainly due to mutations in the target genes (Folp1, RpoB and GyrA). It is important to monitor antimicrobial resistance in patients with leprosy. Therefore, we performed a meta-analysis of drug resistance in Mycobacterium leprae and the mutational profile of the target genes. In this paper, we limited the study period to May 2022 and searched PubMed, Web of Science (WOS), Scopus, and Embase databases for identified studies. Two independent reviewers extracted the study data. Mutation and drug-resistance rates were estimated in Stata 16.0. The results demonstrated that the drug-resistance rate was 10.18% (95% CI: 7.85–12.51). Subgroup analysis showed the highest resistance rate was in the Western Pacific region (17.05%, 95% CI:1.80 to 13.78), and it was higher after 2009 than before [(11.39%, 7.46–15.33) vs. 6.59% (3.66–9.53)]. We can conclude that the rate among new cases (7.25%, 95% CI: 4.65–9.84) was lower than the relapsed (14.26%, 95 CI%: 9.82–18.71). Mutation rates of Folp1, RpoB and GyrA were 4.40% (95% CI: 3.02–5.77), 3.66% (95% CI: 2.41–4.90) and 1.28% (95% CI: 0.87–1.71) respectively, while the rate for polygenes mutation was 1.73% (0.83–2.63). For further analysis, we used 368 drug-resistant strains as research subjects and found that codons (Ser, Pro, Ala) on RpoB, Folp1 and GyrA are the most common mutation sites in the determining region (DRDR). In addition, the most common substitution patterns of Folp1, RpoB, and GyrA are Pro→Leu, Ser→Leu, and Ala→Val. This study found that a higher proportion of patients has developed resistance to these drugs, and the rate has increased since 2009, which continue to pose a challenge to clinicians. In addition, the amino acid alterations in the sequence of the DRDR regions and the substitution patterns mentioned in the study also provide new ideas for clinical treatment options.

Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures

Changes in protein sequence can have dramatic effects on how proteins fold, their stability and dynamics. Over the last 20 years, pioneering methods have been developed to try to estimate the effects of missense mutations on protein stability, leveraging growing availability of protein 3D structures. These, however, have been developed and validated using experimentally derived structures and biophysical measurements. A large proportion of protein structures remain to be experimentally elucidated and, while many studies have based their conclusions on predictions made using homology models, there has been no systematic evaluation of the reliability of these tools in the absence of experimental structural data. We have, therefore, systematically investigated the performance and robustness of ten widely used structural methods when presented with homology models built using templates at a range of sequence identity levels (from 15% to 95%) and contrasted performance with sequence-based tools, as a baseline. We found there is indeed performance deterioration on homology models built using templates with sequence identity below 40%, where sequence-based tools might become preferable. This was most marked for mutations in solvent exposed residues and stabilizing mutations. As structure prediction tools improve, the reliability of these predictors is expected to follow, however we strongly suggest that these factors should be taken into consideration when interpreting results from structure-based predictors of mutation effects on protein stability.

Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures

Large biomolecular structures are being determined experimentally on a daily basis using established techniques such as crystallography and electron microscopy. In addition, emerging integrative or hybrid methods (I/HM) are producing structural models of huge macromolecular machines and assemblies, sometimes containing 100s of millions of non-hydrogen atoms. The performance requirements for visualization and analysis tools delivering these data are increasing rapidly. Significant progress in developing online, web-native three-dimensional (3D) visualization tools was previously accomplished with the introduction of the LiteMol suite and NGL Viewers. Thereafter, Mol* development was jointly initiated by PDBe and RCSB PDB to combine and build on the strengths of LiteMol (developed by PDBe) and NGL (developed by RCSB PDB). The web-native Mol* Viewer enables 3D visualization and streaming of macromolecular coordinate and experimental data, together with capabilities for displaying structure quality, functional, or biological context annotations. High-performance graphics and data management allows users to simultaneously visualise up to hundreds of (superimposed) protein structures, stream molecular dynamics simulation trajectories, render cell-level models, or display huge I/HM structures. It is the primary 3D structure viewer used by PDBe and RCSB PDB. It can be easily integrated into third-party services. Mol* Viewer is open source and freely available at https://molstar.org/.

A systematic review of Mycobacterium leprae DNA gyrase mutations and their impact on fluoroquinolone resistance

BACKGROUND

The fact that Mycobacterium leprae does not grow in vitro remains a challenge in the survey of its antimicrobial resistance (AMR). Mainly molecular methods are used to diagnose AMR in M. leprae to provide reliable data concerning mutations and their impact. Fluoroquinolones (FQs) are efficient for the treatment of leprosy and the main second-line drugs in case of multidrug resistance.

OBJECTIVES

This study aimed at performing a systematic review (a) to characterize all DNA gyrase gene mutations described in clinical isolates of M. leprae, (b) to distinguish between those associated with FQ resistance or susceptibility and (c) to delineate a consensus numbering system for M. leprae GyrA and GyrB.

DATA SOURCES

Data source was PubMed.

STUDY ELIGIBILITY CRITERIA

Publications reporting genotypic susceptibility-testing methods and gyrase gene mutations in M. leprae clinical strains.

RESULTS

In 25 studies meeting our inclusion criteria, 2884 M. leprae isolates were analysed (2236 for gyrA only (77%) and 755 for both gyrA and gyrB (26%)): 3.8% of isolates had gyrA mutations (n = 110), mostly at position 91 (n = 75, 68%) and 0.8% gyrB mutations (n = 6). Since we found discrepancies regarding the location of substitutions associated with FQ resistance, we established a consensus numbering system to properly number the mutations. We also designed a 3D model of the M. leprae DNA gyrase to predict the impact of mutations whose role in FQ-susceptibility has not been demonstrated previously.

CONCLUSIONS

Mutations in DNA gyrase are observed in 4% of the M. leprae clinical isolates. To solve discrepancies among publications and to distinguish between mutations associated with FQ resistance or susceptibility, the consensus numbering system we proposed as well as the 3D model of the M. leprae gyrase for the evaluation of the impact of unknown mutations in FQ resistance, will provide help for resistance surveillance.

mmCSM-PPI: predicting the effects of multiple point mutations on protein-protein interactions

Protein-protein interactions play a crucial role in all cellular functions and biological processes and mutations leading to their disruption are enriched in many diseases. While a number of computational methods to assess the effects of variants on protein-protein binding affinity have been proposed, they are in general limited to the analysis of single point mutations and have been shown to perform poorly on independent test sets. Here, we present mmCSM-PPI, a scalable and effective machine learning model for accurately assessing changes in protein-protein binding affinity caused by single and multiple missense mutations. We expanded our well-established graph-based signatures in order to capture physicochemical and geometrical properties of multiple wild-type residue environments and integrated them with substitution scores and dynamics terms from normal mode analysis. mmCSM-PPI was able to achieve a Pearson's correlation of up to 0.75 (RMSE = 1.64 kcal/mol) under 10-fold cross-validation and 0.70 (RMSE = 2.06 kcal/mol) on a non-redundant blind test, outperforming existing methods. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/mmcsm_ppi.

Distinguishing between PTEN clinical phenotypes through mutation analysis

Phosphate and tensin homolog on chromosome ten (PTEN) germline mutations are associated with an overarching condition known as PTEN hamartoma tumor syndrome. Clinical phenotypes associated with this syndrome range from macrocephaly and autism spectrum disorder to Cowden syndrome, which manifests as multiple noncancerous tumor-like growths (hamartomas), and an increased predisposition to certain cancers. It is unclear, however, the basis by which mutations might lead to these very diverse phenotypic outcomes. Here we show that, by considering the molecular consequences of mutations in PTEN on protein structure and function, we can accurately distinguish PTEN mutations exhibiting different phenotypes. Changes in phosphatase activity, protein stability, and intramolecular interactions appeared to be major drivers of clinical phenotype, with cancer-associated variants leading to the most drastic changes, while ASD and non-pathogenic variants associated with more mild and neutral changes, respectively. Importantly, we show via saturation mutagenesis that more than half of variants of unknown significance could be associated with disease phenotypes, while over half of Cowden syndrome mutations likely lead to cancer. These insights can assist in exploring potentially important clinical outcomes delineated by PTEN variation.

Structure-Guided Computational Approaches to Unravel Druggable Proteomic Landscape of Mycobacterium leprae

Leprosy, caused by Mycobacterium leprae (M. leprae), is treated with a multidrug regimen comprising Dapsone, Rifampicin, and Clofazimine. These drugs exhibit bacteriostatic, bactericidal and anti-inflammatory properties, respectively, and control the dissemination of infection in the host. However, the current treatment is not cost-effective, does not favor patient compliance due to its long duration (12 months) and does not protect against the incumbent nerve damage, which is a severe leprosy complication. The chronic infectious peripheral neuropathy associated with the disease is primarily due to the bacterial components infiltrating the Schwann cells that protect neuronal axons, thereby inducing a demyelinating phenotype. There is a need to discover novel/repurposed drugs that can act as short duration and effective alternatives to the existing treatment regimens, preventing nerve damage and consequent disability associated with the disease. Mycobacterium leprae is an obligate pathogen resulting in experimental intractability to cultivate the bacillus in vitro and limiting drug discovery efforts to repositioning screens in mouse footpad models. The dearth of knowledge related to structural proteomics of M. leprae, coupled with emerging antimicrobial resistance to all the three drugs in the multidrug therapy, poses a need for concerted novel drug discovery efforts. A comprehensive understanding of the proteomic landscape of M. leprae is indispensable to unravel druggable targets that are essential for bacterial survival and predilection of human neuronal Schwann cells. Of the 1,614 protein-coding genes in the genome of M. leprae, only 17 protein structures are available in the Protein Data Bank. In this review, we discussed efforts made to model the proteome of M. leprae using a suite of software for protein modeling that has been developed in the Blundell laboratory. Precise template selection by employing sequence-structure homology recognition software, multi-template modeling of the monomeric models and accurate quality assessment are the hallmarks of the modeling process. Tools that map interfaces and enable building of homo-oligomers are discussed in the context of interface stability. Other software is described to determine the druggable proteome by using information related to the chokepoint analysis of the metabolic pathways, gene essentiality, homology to human proteins, functional sites, druggable pockets and fragment hotspot maps.