Computational saturation mutagenesis to predict structural consequences of systematic mutations in the beta subunit of RNA polymerase in Mycobacterium leprae

Comprehensive mapping of mutations in TDP-43 and α-Synuclein that affect stability and binding

Abnormal aggregation and amyloid inclusions of TAR DNA-binding protein 43 (TDP-43) and α-Synuclein (α-Syn) are frequently co-observed in amyotrophic lateral sclerosis, Parkinson's disease, and Alzheimer's disease. Several reports showed TDP-43 C-terminal domain (CTD) and α-Syn interact with each other and the aggregates of these two proteins colocalized together in different cellular and animal models. Molecular dynamics simulation was conducted to elucidate the stability of the TDP-43 and Syn complex structure. The interfacial mutations in protein complexes changes the stability and binding affinity of the protein that may cause diseases. Here, we have utilized the computational saturation mutagenesis approach including structure-based stability and binding energy calculations to compute the systemic effects of missense mutations of TDP-43 CTD and α-Syn on protein stability and binding affinity. Most of the interfacial mutations of CTD and α-Syn were found to destabilize the protein and reduced the protein binding affinity. The results thus shed light on the functional consequences of missense mutations observed in TDP-43 associated proteinopathies and may provide the mechanisms of co-morbidities involving these two proteins.Communicated by Ramaswamy H. Sarma.

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.

Insight into de-regulation of amino acid feedback inhibition: a focus on structure analysis method

Regulation of amino acid's biosynthetic pathway is of significant importance to maintain homeostasis and cell functions. Amino acids regulate their biosynthetic pathway by end-product feedback inhibition of enzymes catalyzing committed steps of a pathway. Discovery of new feedback resistant enzyme variants to enhance industrial production of amino acids is a key objective in industrial biotechnology. Deregulation of feedback inhibition has been achieved for various enzymes using in vitro and in silico mutagenesis techniques. As enzyme's function, its substrate binding capacity, catalysis activity, regulation and stability are dependent on its structural characteristics, here, we provide detailed structural analysis of all feedback sensitive enzyme targets in amino acid biosynthetic pathways. Current review summarizes information regarding structural characteristics of various enzyme targets and effect of mutations on their structures and functions especially in terms of deregulation of feedback inhibition. Furthermore, applicability of various experimental as well as computational mutagenesis techniques to accomplish feedback resistance has also been discussed in detail to have an insight into various aspects of research work reported in this particular field of study.

Comprehensive deep mutational scanning reveals the pH induced stability and binding differences between SARS-CoV-2 spike RBD and human ACE2

The SARS-CoV-2 spike (S) glycoprotein with its mobile receptor-binding domain (RBD), binds to the human ACE2 receptor and thus facilitates virus entry through low-pH-endosomal pathways. The high degree of SARS-CoV-2 mutability has raised concern among scientists and medical professionals because it created doubt about the effectiveness of drugs and vaccinations designed specifically for COVID-19. In this study, we used computational saturation mutagenesis approach, including structure-based free energy calculations to analyse the effects of the missense mutations on the SARS-CoV-2 S-RBD stability and the S-RBD binding affinity with ACE2 at three different pH (pH 4.5, pH 6.5, and pH 7.4). A total of 3705 mutations in the S-RBD protein were analyzed, and we discovered that most of these mutations destabilize the RBD protein. Specifically, residues G404, G431, G447, A475, and G526 were important for RBD protein stability. In addition, RBD residues Y449, Y489, Y495, Q498, and N487 were critical for the RBD-ACE2 interaction. Next, we found that the distribution of the mean stability changes and mean binding energy changes of RBD due to mutations at both serological and endosomal pH correlated well, indicating the similar effects of mutations. Overall, this computational analysis is useful for understanding the effects of missense mutations in SARS-CoV-2 pathogenesis at different pH.Communicated by Ramaswamy H. Sarma.

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.

Omicron (B.1.1.529) - A new heavily mutated variant: Mapped location and probable properties of its mutations with an emphasis on S-glycoprotein

Omicron, another SARS-CoV-2 variant, has been recorded and reported as a VoC. It has already spread across >30 countries and is a highly mutated variant. We tried to understand the role of mutations in the investigated variants by comparison with previous characterized VoC. We have mapped the mutations in Omicron S-glycoprotein's secondary and tertiary structure landscape using bioinformatics tools and statistical software and developed different models. In addition, we analyzed the effect of diverse mutations in antibody binding regions of the S-glycoprotein on the binding affinity of the investigated antibodies. This study has chosen eight significant mutations in Omicron (D614G, E484A, N501Y, Q493K, K417N, S477N, Y505H G496S), and seven of them are located in the RBD region. We also performed a comparative analysis of the ΔΔG score of these mutations to understand the stabilizing or destabilizing properties of the investigated mutations. The analysis outcome shows that D614G, Q493K, and S477N mutations are stable mutations with ΔΔG scores of 0.351 kcal/mol, 0.470 kcal/mol, and 0.628 kcal/mol, respectively, according to DynaMut estimations. While other mutations (E484A, N501Y, K417N, Y505H, G496S) showed destabilizing results. The D614G, E484A, N501Y, K417N, Y505H, and G496S mutations increased the molecular flexibility of S-glycoprotein to interact with the ACE2 receptor, increasing the variant's infectivity. Our study will contribute to research on the SARS-CoV-2 variant, Omicron, by providing information on the mutational pattern and exciting properties of these eight significant mutations, such as antibody escape and infectivity quotient (stabilizing or destabilizing; increased or decreased molecular flexibility of S-glycoprotein to interact with the human ACE2 receptor).

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.

RosettaSurf-A surface-centric computational design approach

Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprinted with geometric (shape) and chemical (electrostatics) features of the underlying protein structure. Protein surfaces are dependent on their chemical composition and, ultimately determine protein function, acting as the interface that engages in interactions with other molecules. In the past, such representations were utilized to compare protein structures on global and local scales and have shed light on functional properties of proteins. Here we describe RosettaSurf, a surface-centric computational design protocol, that focuses on the molecular surface shape and electrostatic properties as means for protein engineering, offering a unique approach for the design of proteins and their functions. The RosettaSurf protocol combines the explicit optimization of molecular surface features with a global scoring function during the sequence design process, diverging from the typical design approaches that rely solely on an energy scoring function. With this computational approach, we attempt to address a fundamental problem in protein design related to the design of functional sites in proteins, even when structurally similar templates are absent in the characterized structural repertoire. Surface-centric design exploits the premise that molecular surfaces are, to a certain extent, independent of the underlying sequence and backbone configuration, meaning that different sequences in different proteins may present similar surfaces. We benchmarked RosettaSurf on various sequence recovery datasets and showcased its design capabilities by generating epitope mimics that were biochemically validated. Overall, our results indicate that the explicit optimization of surface features may lead to new routes for the design of functional proteins.

The Structural Basis of Mycobacterium tuberculosis RpoB Drug-Resistant Clinical Mutations on Rifampicin Drug Binding

Tuberculosis (TB), caused by the Mycobacterium tuberculosis infection, continues to be a leading cause of morbidity and mortality in developing countries. Resistance to the first-line anti-TB drugs, isoniazid (INH) and rifampicin (RIF), is a major drawback to effective TB treatment. Genetic mutations in the β-subunit of the DNA-directed RNA polymerase (rpoB) are reported to be a major reason of RIF resistance. However, the structural basis and mechanisms of these resistant mutations are insufficiently understood. In the present study, thirty drug-resistant mutants of rpoB were initially modeled and screened against RIF via a comparative molecular docking analysis with the wild-type (WT) model. These analyses prioritized six mutants (Asp441Val, Ser456Trp, Ser456Gln, Arg454Gln, His451Gly, and His451Pro) that showed adverse binding affinities, molecular interactions, and RIF binding hinderance properties, with respect to the WT. These mutant models were subsequently analyzed by molecular dynamics (MD) simulations. One-hundred nanosecond all-atom MD simulations, binding free energy calculations, and a dynamic residue network analysis (DRN) were employed to exhaustively assess the impact of mutations on RIF binding dynamics. Considering the global structural motions and protein-ligand binding affinities, the Asp441Val, Ser456Gln, and His454Pro mutations generally yielded detrimental effects on RIF binding. Locally, we found that the electrostatic contributions to binding, particularly by Arg454 and Glu487, might be adjusted to counteract resistance. The DRN analysis revealed that all mutations mostly distorted the communication values of the critical hubs and may, therefore, confer conformational changes in rpoB to perturb RIF binding. In principle, the approach combined fundamental molecular modeling tools for robust "global" and "local" level analyses of structural dynamics, making it well suited for investigating other similar drug resistance cases.

Design, synthesis, biological screening and molecular docking studies of novel multifunctional 1,4-di (aryl/heteroaryl) substituted piperazine derivatives as potential antitubercular and antimicrobial agents

In this paper, two series of novel multifunctional 1, 4-di (aryl/heteroaryl) substituted piperazine derivatives (6a-d & 7a-d) were synthesized, characterized, and evaluated for their antitubercular, antibacterial, and antifungal activities. A step-wise reduction, bromination and substitution reactions on various aldehydes resulted in alcohols (2a-d), bromides (3a-d), and titled novel compounds (6a-d & 7a-d) in moderate to good yields (48-85%). The novel compounds were evaluated for their antitubercular and antimicrobial activities. Compound 7a exhibited promising antitubercular activity (MIC: 0.65 µg/mL) almost equal to the Rifampicin, while the rest of the compounds were moderately active against MTB H37Rv except 6b. Compounds 7a and 6b showed good activity against tested fungal pathogens. Compounds 7a and 7b were proven as the best bacterial agents. Molecular docking studies were in agreement with the in-vitro results. Docking analyses show that all the synthesized molecules bind to the target protein Mtb RNAP (PDB ID: 5UHC) fairly strongly. All the compounds were evaluated for their in vitro cytotoxicity effect using the MTT assay method against human cancer cell line MCF-7. The compounds demonstrated growth inhibitory effect on the cell line with significant IC₅₀ values ranging between 8.20 and 34.45 µM. Most importantly, compound 7a displayed good binding affinity towards the tested protein with binding energy -7.30 kcal/mol and a stronger hydrogen bond distance of 2.2 Å with ASN-493 residue. Thus, the present research highlighted the potential role of novel piperazine derivatives as potential antitubercular, and antimicrobial candidates and further good research into optimization might result in the development of new antitubercular drug candidates.

Novel dynamic residue network analysis approaches to study allosteric modulation: SARS-CoV-2 Mpro and its evolutionary mutations as a case study

The rational search for allosteric modulators and the allosteric mechanisms of these modulators in the presence of mutations is a relatively unexplored field. Here, we established novel in silico approaches and applied them to SARS-CoV-2 main protease (Mpro) as a case study. First, we identified six potential allosteric modulators. Then, we focused on understanding the allosteric effects of these modulators on each of its protomers. We introduced a new combinatorial approach and dynamic residue network (DRN) analysis algorithms to examine patterns of change and conservation of critical nodes, according to five independent criteria of network centrality. We observed highly conserved network hubs for each averaged DRN metric on the basis of their existence in both protomers in the absence and presence of all ligands (persistent hubs). We also detected ligand specific signal changes. Using eigencentrality (EC) persistent hubs and ligand introduced hubs we identified a residue communication path connecting the allosteric binding site to the catalytic site. Finally, we examined the effects of the mutations on the behavior of the protein in the presence of selected potential allosteric modulators and investigated the ligand stability. One crucial outcome was to show that EC centrality hubs form an allosteric communication path between the allosteric ligand binding site to the active site going through the interface residues of domains I and II; and this path was either weakened or lost in the presence of some of the mutations. Overall, the results revealed crucial aspects that need to be considered in rational computational drug discovery.

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.

Anatomy of noncovalent interactions between the nucleobases or ribose and π-containing amino acids in RNA-protein complexes

A set of >300 nonredundant high-resolution RNA–protein complexes were rigorously searched for π-contacts between an amino acid side chain (W, H, F, Y, R, E and D) and an RNA nucleobase (denoted π–π interaction) or ribose moiety (denoted sugar–π). The resulting dataset of >1500 RNA–protein π-contacts were visually inspected and classified based on the interaction type, and amino acids and RNA components involved. More than 80% of structures searched contained at least one RNA–protein π-interaction, with π–π contacts making up 59% of the identified interactions. RNA–protein π–π and sugar–π contacts exhibit a range in the RNA and protein components involved, relative monomer orientations and quantum mechanically predicted binding energies. Interestingly, π–π and sugar–π interactions occur more frequently with RNA (4.8 contacts/structure) than DNA (2.6). Moreover, the maximum stability is greater for RNA–protein contacts than DNA–protein interactions. In addition to highlighting distinct differences between RNA and DNA–protein binding, this work has generated the largest dataset of RNA–protein π-interactions to date, thereby underscoring that RNA–protein π-contacts are ubiquitous in nature, and key to the stability and function of RNA–protein complexes.

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

Computational Saturation Mutagenesis is an in-silico approach that employs systematic mutagenesis of each amino acid residue in the protein to all other amino acid types, and predicts changes in thermodynamic stability and affinity to the other subunits/protein counterparts, ligands and nucleic acid molecules. The data thus generated are useful in understanding the functional consequences of mutations in antimicrobial resistance phenotypes. In this study, we applied computational saturation mutagenesis to three important drug-targets in Mycobacterium leprae (M. leprae) for the drugs dapsone, rifampin and ofloxacin namely Dihydropteroate Synthase (DHPS), RNA Polymerase (RNAP) and DNA Gyrase (GYR), respectively. M. leprae causes leprosy and is an obligate intracellular bacillus with limited protein structural information associating mutations with phenotypic resistance outcomes in leprosy. Experimentally solved structures of DHPS, RNAP and GYR of M. leprae are not available in the Protein Data Bank, therefore, we modelled the structures of these proteins using template-based comparative modelling and introduced systematic mutations in each model generating 80,902 mutations and mutant structures for all the three proteins. Impacts of mutations on stability and protein-subunit, protein-ligand and protein-nucleic acid affinities were computed using various in-house developed and other published protein stability and affinity prediction software. A consensus impact was estimated for each mutation using qualitative scoring metrics for physicochemical properties and by a categorical grouping of stability and affinity predictions. We developed a web database named HARP (a database of Hansen's Disease Antimicrobial Resistance Profiles), which is accessible at the URL - https://harp-leprosy.org and provides the details to each of these predictions.

Combining structure and genomics to understand antimicrobial resistance

Antimicrobials against bacterial, viral and parasitic pathogens have transformed human and animal health. Nevertheless, their widespread use (and misuse) has led to the emergence of antimicrobial resistance (AMR) which poses a potentially catastrophic threat to public health and animal husbandry. There are several routes, both intrinsic and acquired, by which AMR can develop. One major route is through non-synonymous single nucleotide polymorphisms (nsSNPs) in coding regions. Large scale genomic studies using high-throughput sequencing data have provided powerful new ways to rapidly detect and respond to such genetic mutations linked to AMR. However, these studies are limited in their mechanistic insight. Computational tools can rapidly and inexpensively evaluate the effect of mutations on protein function and evolution. Subsequent insights can then inform experimental studies, and direct existing or new computational methods. Here we review a range of sequence and structure-based computational tools, focussing on tools successfully used to investigate mutational effect on drug targets in clinically important pathogens, particularly Mycobacterium tuberculosis. Combining genomic results with the biophysical effects of mutations can help reveal the molecular basis and consequences of resistance development. Furthermore, we summarise how the application of such a mechanistic understanding of drug resistance can be applied to limit the impact of AMR.

Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches

Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce protein affinities within the RNA polymerase complex, subsequently reducing nucleic acid affinity. Here, we have used these insights to develop a computational rifampicin resistance predictor capable of identifying resistant mutations even outside the well-defined rifampicin resistance determining region (RRDR), using clinical M. tuberculosis sequencing information. Our tool successfully identified up to 90.9% of M. tuberculosis rpoB variants correctly, with sensitivity of 92.2%, specificity of 83.6% and MCC of 0.69, outperforming the current gold-standard GeneXpert-MTB/RIF. We show our model can be translated to other clinically relevant organisms: M. leprae, P. aeruginosa and S. aureus, despite weak sequence identity. Our method was implemented as an interactive tool, SUSPECT-RIF (StrUctural Susceptibility PrEdiCTion for RIFampicin), freely available at https://biosig.unimelb.edu.au/suspect_rif/ .

Systemic effects of missense mutations on SARS-CoV-2 spike glycoprotein stability and receptor-binding affinity

The spike (S) glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the binding to the permissive cells. The receptor-binding domain (RBD) of SARS-CoV-2 S protein directly interacts with the human angiotensin-converting enzyme 2 (ACE2) on the host cell membrane. In this study, we used computational saturation mutagenesis approaches, including structure-based energy calculations and sequence-based pathogenicity predictions, to quantify the systemic effects of missense mutations on SARS-CoV-2 S protein structure and function. A total of 18 354 mutations in S protein were analyzed, and we discovered that most of these mutations could destabilize the entire S protein and its RBD. Specifically, residues G431 and S514 in SARS-CoV-2 RBD are important for S protein stability. We analyzed 384 experimentally verified S missense variations and revealed that the dominant pandemic form, D614G, can stabilize the entire S protein. Moreover, many mutations in N-linked glycosylation sites can increase the stability of the S protein. In addition, we investigated 3705 mutations in SARS-CoV-2 RBD and 11 324 mutations in human ACE2 and found that SARS-CoV-2 neighbor residues G496 and F497 and ACE2 residues D355 and Y41 are critical for the RBD-ACE2 interaction. The findings comprehensively provide potential target sites in the development of drugs and vaccines against COVID-19.

mCSM-membrane: predicting the effects of mutations on transmembrane proteins

Significant efforts have been invested into understanding and predicting the molecular consequences of mutations in protein coding regions, however nearly all approaches have been developed using globular, soluble proteins. These methods have been shown to poorly translate to studying the effects of mutations in membrane proteins. To fill this gap, here we report, mCSM-membrane, a user-friendly web server that can be used to analyse the impacts of mutations on membrane protein stability and the likelihood of them being disease associated. mCSM-membrane derives from our well-established mutation modelling approach that uses graph-based signatures to model protein geometry and physicochemical properties for supervised learning. Our stability predictor achieved correlations of up to 0.72 and 0.67 (on cross validation and blind tests, respectively), while our pathogenicity predictor achieved a Matthew's Correlation Coefficient (MCC) of up to 0.77 and 0.73, outperforming previously described methods in both predicting changes in stability and in identifying pathogenic variants. mCSM-membrane will be an invaluable and dedicated resource for investigating the effects of single-point mutations on membrane proteins through a freely available, user friendly web server at http://biosig.unimelb.edu.au/mcsm_membrane.

DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations

Predicting the effect of missense variations on protein stability and dynamics is important for understanding their role in diseases, and the link between protein structure and function. Approaches to estimate these changes have been proposed, but most only consider single‐point missense variants and a static state of the protein, with those that incorporate dynamics are computationally expensive. Here we present DynaMut2, a web server that combines Normal Mode Analysis (NMA) methods to capture protein motion and our graph‐based signatures to represent the wildtype environment to investigate the effects of single and multiple point mutations on protein stability and dynamics. DynaMut2 was able to accurately predict the effects of missense mutations on protein stability, achieving Pearson's correlation of up to 0.72 (RMSE: 1.02 kcal/mol) on a single point and 0.64 (RMSE: 1.80 kcal/mol) on multiple‐point missense mutations across 10‐fold cross‐validation and independent blind tests. For single‐point mutations, DynaMut2 achieved comparable performance with other methods when predicting variations in Gibbs Free Energy (ΔΔG) and in melting temperature (ΔT_m). We anticipate our tool to be a valuable suite for the study of protein flexibility analysis and the study of the role of variants in disease. DynaMut2 is freely available as a web server and API at http://biosig.unimelb.edu.au/dynamut2.

Genomics, Computational Biology and Drug Discovery for Mycobacterial Infections: Fighting the Emergence of Resistance

Tuberculosis (TB) and leprosy are mycobacterial infections caused by Mycobacterium tuberculosis and Mycobacterium leprae respectively. These diseases continue to be endemic in developing countries where the cost of new medicines presents major challenges. The situation is further exacerbated by the emergence of resistance to many front-line antibiotics. A priority now is to design new antimycobacterials that are not only effective in combatting the diseases but are also less likely to give rise to resistance. In both these respects understanding the structure of drug targets in M. tuberculosis and M. leprae is crucial. In this review we describe structure-guided approaches to understanding the impacts of mutations that give rise to antimycobacterial resistance and the use of this information in the design of new medicines.

A Personal History of Using Crystals and Crystallography to Understand Biology and Advanced Drug Discovery

Over the past 60 years, the use of crystals to define structures of complexes using X-ray analysis has contributed to the discovery of new medicines in a very significant way. This has been in understanding not only small-molecule inhibitors of proteins, such as enzymes, but also protein or peptide hormones or growth factors that bind to cell surface receptors. Experimental structures from crystallography have also been exploited in software to allow prediction of structures of important targets based on knowledge of homologues. Crystals and crystallography continue to contribute to drug design and provide a successful example of academia–industry collaboration.

mmCSM-AB: guiding rational antibody engineering through multiple point mutations

While antibodies are becoming an increasingly important therapeutic class, especially in personalized medicine, their development and optimization has been largely through experimental exploration. While there have been many efforts to develop computational tools to guide rational antibody engineering, most approaches are of limited accuracy when applied to antibody design, and have largely been limited to analysing a single point mutation at a time. To overcome this gap, we have curated a dataset of 242 experimentally determined changes in binding affinity upon multiple point mutations in antibody-target complexes (89 increasing and 153 decreasing binding affinity). Here, we have shown that by using our graph-based signatures and atomic interaction information, we can accurately analyse the consequence of multi-point mutations on antigen binding affinity. Our approach outperformed other available tools across cross-validation and two independent blind tests, achieving Pearson's correlations of up to 0.95. We have implemented our new approach, mmCSM-AB, as a web-server that can help guide the process of affinity maturation in antibody design. mmCSM-AB is freely available at http://biosig.unimelb.edu.au/mmcsm_ab/.