Cancer missense mutations alter binding properties of proteins and their interaction networks

Establishment and molecular characterization of the novel cutaneous squamous cell carcinoma cell line from advanced-stage Indian patient

Cutaneous squamous cell carcinoma (CSCC) is the second most prevalent skin cancer with low metastatic potential; it poses significant morbidity challenges. CSCC possesses significant heterogeneity and the treatment presents a formidable challenge. To gain a clear insight into the diverse nature of these tumors, the development of an in vitro cell line model is essential. However, there are few cell lines that were established, and only one skin SCC cell line is available on the ATCC. In the present study, we established and characterized a novel ACSCC1 cell line from the advanced-stage treatment naïve cutaneous SCC originating from the forearm of the Indian patient. The keratin expression profile showed the epithelial origin of the cell line, ploidy and karyotyping revealed the hyperdiploid population; ACSCC1 showed an increased tumorigenic and metastatic potential. Further, our cell line showed higher invasive, migratory potential and epithelial-mesenchymal-transition (EMT). Additionally, the Transmission electron microscopy (TEM) results showed an aberrant mitochondrial morphology and reduction in the cellular junctions. Further, our whole genome sequencing (WGS) analysis showed mutations in the cancer-related genes. Overall, the novel ACSCC1 cell line can be used to decipher the molecular signaling in the cancer stem cells (CSCs); targeting the CSCs population may help in understanding the tumor recurrence.

Role of Mutual Information Profile Shifts in Assessing the Pathogenicity of Mutations on Protein Functions: The Case of Pyrin Variants Associated With Familial Mediterranean Fever

This study presents a novel method to assess the pathogenicity of pyrin protein mutations by using mutual information (MI) as a measure to quantify the correlation between residue motions or fluctuations and associated changes affecting the phenotype. The concept of MI profile shift is presented to quantify changes in MI upon mutation, revealing insights into residue-residue interactions at critical positions. We apply this method to the pyrin protein variants, which are associated with an autosomal recessively inherited disease called familial Mediterranean fever (FMF) since the available tools do not help predict the pathogenicity of the most penetrant variants. We demonstrate the utility of MI profile shifts in assessing the effects of mutations on protein stability, function, and disease phenotype. The importance of MI shifts, particularly the negative shifts observed in the pyrin example, as indicators of severe functional effects is emphasized. Additionally, the exploration of potential compensatory mechanisms suggested by positive MI shifts, which are otherwise random and inconsequential, is highlighted. The study also discusses challenges in relating MI profile changes to disease severity and advocates for comprehensive analysis considering genetic, environmental, and stochastic factors. Overall, this study provides insights into the molecular mechanisms underlying the pathogenesis of FMF and offers a framework for identifying potential therapeutic targets based on MI profile changes induced by mutations.

Strategies for in Silico Drug Discovery to Modulate Macromolecular Interactions Altered by Mutations

Most human diseases have genetic components, frequently single nucleotide variants (SNVs), which alter the wild type characteristics of macromolecules and their interactions. A straightforward approach for correcting such SNVs-related alterations is to seek small molecules, potential drugs, that can eliminate disease-causing effects. Certain disorders are caused by altered protein-protein interactions, for example, Snyder-Robinson syndrome, the therapy for which focuses on the development of small molecules that restore the wild type homodimerization of spermine synthase. Other disorders originate from altered protein-nucleic acid interactions, as in the case of cancer; in these cases, the elimination of disease-causing effects requires small molecules that eliminate the effect of mutation and restore wild type p53-DNA affinity. Overall, especially for complex diseases, pathogenic mutations frequently alter macromolecular interactions. This effect can be direct, i.e., the alteration of wild type affinity and specificity, or indirect via alterations in the concentration of the binding partners. Here, we outline progress made in methods and strategies to computationally identify small molecules capable of altering macromolecular interactions in a desired manner, reducing or increasing the binding affinity, and eliminating the disease-causing effect. When applicable, we provide examples of the outlined general strategy. Successful cases are presented at the end of the work.

Decoding the effects of mutation on protein interactions using machine learning

Accurately predicting mutation-caused binding free energy changes (ΔΔGs) on protein interactions is crucial for understanding how genetic variations affect interactions between proteins and other biomolecules, such as proteins, DNA/RNA, and ligands, which are vital for regulating numerous biological processes. Developing computational approaches with high accuracy and efficiency is critical for elucidating the mechanisms underlying various diseases, identifying potential biomarkers for early diagnosis, and developing targeted therapies. This review provides a comprehensive overview of recent advancements in predicting the impact of mutations on protein interactions across different interaction types, which are central to understanding biological processes and disease mechanisms, including cancer. We summarize recent progress in predictive approaches, including physicochemical-based, machine learning, and deep learning methods, evaluating the strengths and limitations of each. Additionally, we discuss the challenges related to the limitations of mutational data, including biases, data quality, and dataset size, and explore the difficulties in developing accurate prediction tools for mutation-induced effects on protein interactions. Finally, we discuss future directions for advancing these computational tools, highlighting the capabilities of advancing technologies, such as artificial intelligence to drive significant improvements in mutational effects prediction.

Identification of molecular targets of Trigonelline for treating breast cancer through network pharmacology and bioinformatics-based prediction

Breast cancer, a highly prevalent and fatal cancer that affects the female population worldwide, stands as a significant health challenge. Despite the abundance of chemotherapy drugs, the adverse side effects associated with them have initiated an investigation into natural plant-based compounds. Trigonelline, an alkaloid found in Trigonella foenum-graecum, was previously reported for its anticancer properties by the researchers. In this present study, we have identified the molecular targets of Trigonelline in breast cancer and predicted its drug-like properties and toxicity. By analyzing breast cancer targets from databases including TTD, TCGA, Gene cards, and Trigonelline targets obtained from CTD, we identified 14 specific targets of Trigonelline in the context of breast cancer. The protein-protein interaction (PPI) network of the 14 Trigonelline targets provided insights into the complex relationships between different genes and targets. Heatmap analysis demonstrated the expression patterns of these 14 genes at the protein and RNA levels in breast cancer cells and breast tissues. Notably, four genes, namely EGF, BAX, EGFR, and MTOR, were enriched in the breast cancer pathway. At the same time, PARP1, DDIT3, BAX, and TNF were associated with the apoptosis pathway according to KEGG pathway enrichment analyses. Molecular docking studies between Trigonelline and target proteins from the Protein Data Bank (PDB) revealed favorable binding affinity. Furthermore, mutation analysis of target genes within a dataset of 1918 samples from cBioPortal revealed the absence of mutations. Remarkably, Trigonelline also exhibited binding affinity towards two mutant proteins, and based on these findings, we predicted that Trigonelline could be utilized to target breast cancer genes and their mutants through network pharmacology. Additionally, this was supported by molecular dynamic simulation studies. As our study is preliminary, further validation through in vitro and in vivo studies is essential to confirm the efficacy of Trigonelline in breast cancer treatment.

AAindexNC: Estimating the Physicochemical Properties of Non-Canonical Amino Acids, Including Those Derived from the PDB and PDBeChem Databank

The physicochemical properties of amino acid residues from the AAindex database are widely used as predictors in building models for predicting both protein structures and properties. It should be noted, however, that the AAindex database contains data only for the 20 canonical amino acids. Non-canonical amino acids, while less common, are not rare; the Protein Data Bank includes proteins with more than 1000 distinct non-canonical amino acids. In this study, we propose a method to evaluate the physicochemical properties from the AAindex database for non-canonical amino acids and assess the prediction quality. We implemented our method as a bioinformatics tool and estimated the physicochemical properties of non-canonical amino acids from the PDB with the chemical composition presentation using SMILES encoding obtained from the PDBechem databank. The bioinformatics tool and resulting database of the estimated properties are freely available on the author's website and available for download via GitHub.

Pathogenic nsSNPs of protein kinase C-eta with hepatocellular carcinoma susceptibility

BACKGROUND

Hepatocellular carcinoma (HCC) is a global health concern. Due to late diagnosis and limited therapeutic strategies, HCC based mortality rate is exponentially increasing globally. Genetic predisposition is a non-avoidable intrinsic factor that could alter the genome sequence, ultimately leading to HCC. Protein kinase C eta (PKCη) is involved in key physiological roles, hence alteration in PKCη could aid in cancer progression. Research indicates association between non-synonymous (ns) SNPs and HCC onset. However, effect of nsSNP variants of PKCη on HCC development has not been explored yet. Hence, this study aimed to investigate the association between pathogenic nsSNPs of PKCη with HCC.

METHODS

Non-synonymous (missense) variants of PKCη were obtained from Ensembl genome browser. These variants were filtered out to obtain pathogenic nsSNPs of PKCη. Genotyping of nsSNPs was done through Tetra ARMS PCR. For that, blood samples of 348 HCC patients and 337 controls were collected. The clinical factors that influence HCC were studied. Relative risk (RR) and Odds Ratio (OR) with 95% confidence interval was calculated by Chi-square test and P-value < 0.05 was deemed significant.

RESULTS

Five nsSNP variants of PKCη including rs1162102190 (T/C), rs868127012 (G/T), rs750830348 (G/T), rs768619375 (T/C), and rs752329416 (T/C) were identified. The retrieved nsSNPs were frequently identified in HCC patients. However, rs752329416 T/C was significantly prevalent in patients having HCC family history. Moreover, all the variants were found in HCC patients manifesting the stage II than the advance stages of HCC.

CONCLUSION

This study can be utilized to identify potential genetic markers for early screening of HCC. Moreover, consideration of further clinical factors, and mechanistic approach would enhance the understanding that how alteration in nsSNPs could impact the HCC onset.

Molecular chaperones: Guardians of tumor suppressor stability and function

The term 'tumor suppressor' describes a widely diverse set of genes that are generally involved in the suppression of metastasis, but lead to tumorigenesis upon loss-of-function mutations. Despite the protein products of tumor suppressors exhibiting drastically different structures and functions, many share a common regulatory mechanism-they are molecular chaperone 'clients'. Clients of molecular chaperones depend on an intracellular network of chaperones and co-chaperones to maintain stability. Mutations of tumor suppressors that disrupt proper chaperoning prevent the cell from maintaining sufficient protein levels for physiological function. This review discusses the role of the molecular chaperones Hsp70 and Hsp90 in maintaining the stability and functional integrity of tumor suppressors. The contribution of cochaperones prefoldin, HOP, Aha1, p23, FNIP1/2 and Tsc1 as well as the chaperonin TRiC to tumor suppressor stability is also discussed. Genes implicated in renal cell carcinoma development-VHL, TSC1/2, and FLCN-will be used as examples to explore this concept, as well as how pathogenic mutations of tumor suppressors cause disease by disrupting protein chaperoning, maturation, and function.

Strength of selection in lung tumors correlates with clinical features better than tumor mutation burden

Single nucleotide substitutions are the most common type of somatic mutations in cancer genome. The goal of this study was to use publicly available somatic mutation data to quantify negative and positive selection in individual lung tumors and test how strength of directional and absolute selection is associated with clinical features. The analysis found a significant variation in strength of selection (both negative and positive) among tumors, with median selection tending to be negative even though tumors with strong positive selection also exist. Strength of selection estimated as the density of missense mutations relative to the density of silent mutations showed only a weak correlation with tumor mutation burden. In the "all histology together" analysis we found that absolute strength of selection was strongly correlated with all clinically relevant features analyzed. In histology-stratified analysis selection was strongest in small cell lung cancer. Selection in adenocarcinoma was somewhat higher compared to squamous cell carcinoma. The study suggests that somatic mutation- based quantifying of directional and absolute selection in individual tumors can be a useful biomarker of tumor aggressiveness.

DeepPPAPredMut: deep ensemble method for predicting the binding affinity change in protein-protein complexes upon mutation

MOTIVATION

Protein-protein interactions underpin many cellular processes and their disruption due to mutations can lead to diseases. With the evolution of protein structure prediction methods like AlphaFold2 and the availability of extensive experimental affinity data, there is a pressing need for updated computational tools that can efficiently predict changes in binding affinity caused by mutations in protein-protein complexes.

RESULTS

We developed a deep ensemble model that leverages protein sequences, predicted structure-based features, and protein functional classes to accurately predict the change in binding affinity due to mutations. The model achieved a correlation of 0.97 and a mean absolute error (MAE) of 0.35 kcal/mol on the training dataset, and maintained robust performance on the test set with a correlation of 0.72 and a MAE of 0.83 kcal/mol. Further validation using Leave-One-Out Complex (LOOC) cross-validation exhibited a correlation of 0.83 and a MAE of 0.51 kcal/mol, indicating consistent performance.

AVAILABILITY AND IMPLEMENTATION

https://web.iitm.ac.in/bioinfo2/DeepPPAPredMut/index.html.

Computational analysis of protein-protein interactions of cancer drivers in renal cell carcinoma

Renal cell carcinoma (RCC) is the most common type of kidney cancer with rising cases in recent years. Extensive research has identified various cancer driver proteins associated with different subtypes of RCC. Most RCC drivers are encoded by tumor suppressor genes and exhibit enrichment in functional categories such as protein degradation, chromatin remodeling, and transcription. To further our understanding of RCC, we utilized powerful deep-learning methods based on AlphaFold to predict protein-protein interactions (PPIs) involving RCC drivers. We predicted high-confidence complexes formed by various RCC drivers, including TCEB1, KMT2C/D and KDM6A of the COMPASS-related complexes, TSC1 of the MTOR pathway, and TRRAP. These predictions provide valuable structural insights into the interaction interfaces, some of which are promising targets for cancer drug design, such as the NRF2-MAFK interface. Cancer somatic missense mutations from large datasets of genome sequencing of RCCs were mapped to the interfaces of predicted and experimental structures of PPIs involving RCC drivers, and their effects on the binding affinity were evaluated. We observed more than 100 cancer somatic mutations affecting the binding affinity of complexes formed by key RCC drivers such as VHL and TCEB1. These findings emphasize the importance of these mutations in RCC pathogenesis and potentially offer new avenues for targeted therapies.

Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation

Accurate prediction of binding free energy changes upon mutations is vital for optimizing drugs, designing proteins, understanding genetic diseases, and cost-effective virtual screening. While machine learning methods show promise in this domain, achieving accuracy and generalization across diverse data sets remains a challenge. This study introduces Geometric Graph Learning for Protein-Protein Interactions (GGL-PPI), a novel approach integrating geometric graph representation and machine learning to forecast mutation-induced binding free energy changes. GGL-PPI leverages atom-level graph coloring and multiscale weighted colored geometric subgraphs to capture structural features of biomolecules, demonstrating superior performance on three standard data sets, namely, AB-Bind, SKEMPI 1.0, and SKEMPI 2.0 data sets. The model's efficacy extends to predicting protein thermodynamic stability in a blind test set, providing unbiased predictions for both direct and reverse mutations and showcasing notable generalization. GGL-PPI's precision in predicting changes in binding free energy and stability due to mutations enhances our comprehension of protein complexes, offering valuable insights for drug design endeavors.

Heterogeneous splicing patterns resulting from KIF5A variants associated with amyotrophic lateral sclerosis

Single-nucleotide variants (SNVs) in the gene encoding Kinesin Family Member 5A (KIF5A), a neuronal motor protein involved in anterograde transport along microtubules, have been associated with amyotrophic lateral sclerosis (ALS). ALS is a rapidly progressive and fatal neurodegenerative disease that primarily affects the motor neurons. Numerous ALS-associated KIF5A SNVs are clustered near the splice-site junctions of the penultimate exon 27 and are predicted to alter the carboxy-terminal (C-term) cargo-binding domain of KIF5A. Mis-splicing of exon 27, resulting in exon exclusion, is proposed to be the mechanism by which these SNVs cause ALS. Whether all SNVs proximal to exon 27 result in exon exclusion is unclear. To address this question, we designed an in vitro minigene splicing assay in human embryonic kidney 293 cells, which revealed heterogeneous site-specific effects on splicing: only 5' splice-site (5'ss) SNVs resulted in exon skipping. We also quantified splicing in select clustered, regularly interspaced, short palindromic repeats-edited human stem cells, differentiated to motor neurons, and in neuronal tissues from a 5'ss SNV knock-in mouse, which showed the same result. Moreover, the survival of representative 3' splice site, 5'ss, and truncated C-term variant KIF5A (v-KIF5A) motor neurons was severely reduced compared with wild-type motor neurons, and overt morphological changes were apparent. While the total KIF5A mRNA levels were comparable across the cell lines, the total KIF5A protein levels were decreased for v-KIF5A lines, suggesting an impairment of protein synthesis or stability. Thus, despite the heterogeneous effect on ribonucleic acid splicing, KIF5A SNVs similarly reduce the availability of the KIF5A protein, leading to axonal transport defects and motor neuron pathology.

ASCARIS: Positional feature annotation and protein structure-based representation of single amino acid variations

Background

Genomic variations may cause deleterious effects on protein functionality and perturb biological processes. Elucidating the effects of variations is critical for developing novel treatment strategies for diseases of genetic origin. Computational approaches have been aiding the work in this field by modeling and analyzing the mutational landscape. However, new approaches are required, especially for accurate representation and data-centric analysis of sequence variations.

Method

In this study, we propose ASCARIS (Annotation and StruCture-bAsed RepresentatIon of Single amino acid variations), a method for the featurization (i.e., quantitative representation) of single amino acid variations (SAVs), which could be used for a variety of purposes, such as predicting their functional effects or building multi-omics-based integrative models. ASCARIS utilizes the direct and spatial correspondence between the location of the SAV on the sequence/structure and 30 different types of positional feature annotations (e.g., active/lipidation/glycosylation sites; calcium/metal/DNA binding, inter/transmembrane regions, etc.), along with structural features and physicochemical properties. The main novelty of this method lies in constructing reusable numerical representations of SAVs via functional annotations.

Results

We statistically analyzed the relationship between these features and the consequences of variations and found that each carries information in this regard. To investigate potential applications of ASCARIS, we trained variant effect prediction models that utilize our SAV representations as input. We carried out an ablation study and a comparison against the state-of-the-art methods and observed that ASCARIS has a competing and complementary performance against widely-used predictors. ASCARIS can be used alone or in combination with other approaches to represent SAVs from a functional perspective. ASCARIS is available as a programmatic tool at https://github.com/HUBioDataLab/ASCARIS and as a web-service at https://huggingface.co/spaces/HUBioDataLab/ASCARIS.

Exploring the prognostic significance of PKCε variants in cervical cancer

BACKGROUND

Protein Kinase C-epsilon (PKCε) is a member of the novel subfamily of PKCs (nPKCs) that plays a role in cancer development. Studies have revealed that its elevated expression levels are associated with cervical cancer. Previously, we identified pathogenic variations in its different domains through various bioinformatics tools and molecular dynamic simulation. In the present study, the aim was to find the association of its variants rs1553369874 and rs1345511001 with cervical cancer and to determine the influence of these variants on the protein-protein interactions of PKCε, which can lead towards cancer development and poor survival rates.

METHODS

The association of the variants with cervical cancer and its clinicopathological features was determined through genotyping analysis. Odds ratio and relative risk along with Fisher exact test were calculated to evaluate variants significance and disease risk. Protein-protein docking was performed and docked complexes were subjected to molecular dynamics simulation to gauge the variants impact on PKCε's molecular interactions.

RESULTS

This study revealed that genetic variants rs1553369874 and rs1345511001 were associated with cervical cancer. Smad3 interacts with PKCε and this interaction promotes cervical cancer angiogenesis; therefore, Smad3 was selected for protein-protein docking. The analysis revealed PKCε variants promoted aberrant interactions with Smad3 that might lead to the activation of oncogenic pathways. The data obtained from this study suggested the prognostic significance of PRKCE gene variants rs1553369874 and rs1345511001.

CONCLUSION

Through further in vitro and in vivo validation, these variants can be used at the clinical level as novel prognostic markers and therapeutic targets against cervical cancer.

A mutation-induced drug resistance database (MdrDB)

Mutation-induced drug resistance is a significant challenge to the clinical treatment of many diseases, as structural changes in proteins can diminish drug efficacy. Understanding how mutations affect protein-ligand binding affinities is crucial for developing new drugs and therapies. However, the lack of a large-scale and high-quality database has hindered the research progresses in this area. To address this issue, we have developed MdrDB, a database that integrates data from seven publicly available datasets, which is the largest database of its kind. By integrating information on drug sensitivity and cell line mutations from Genomics of Drug Sensitivity in Cancer and DepMap, MdrDB has substantially expanded the existing drug resistance data. MdrDB is comprised of 100,537 samples of 240 proteins (which encompass 5119 total PDB structures), 2503 mutations, and 440 drugs. Each sample brings together 3D structures of wild type and mutant protein-ligand complexes, binding affinity changes upon mutation (ΔΔG), and biochemical features. Experimental results with MdrDB demonstrate its effectiveness in significantly enhancing the performance of commonly used machine learning models when predicting ΔΔG in three standard benchmarking scenarios. In conclusion, MdrDB is a comprehensive database that can advance the understanding of mutation-induced drug resistance, and accelerate the discovery of novel chemicals.

Cancer driver mutations: predictions and reality

Cancer cells accumulate many genetic alterations throughout their lifetime, but only a few of them drive cancer progression, termed driver mutations. Driver mutations may vary between cancer types and patients, can remain latent for a long time and become drivers at particular cancer stages, or may drive oncogenesis only in conjunction with other mutations. The high mutational, biochemical, and histological tumor heterogeneity makes driver mutation identification very challenging. In this review we summarize recent efforts to identify driver mutations in cancer and annotate their effects. We underline the success of computational methods to predict driver mutations in finding novel cancer biomarkers, including in circulating tumor DNA (ctDNA). We also report on the boundaries of their applicability in clinical research.

PROT-ON: A structure-based detection of designer PROTein interface MutatiONs

The mutation-induced changes across protein-protein interfaces have often been observed to lead to severe diseases. Therefore, several computational tools have been developed to predict the impact of such mutations. Among these tools, FoldX and EvoEF1 stand out as fast and accurate alternatives. Expanding on the capabilities of these tools, we have developed the PROT-ON (PROTein-protein interface mutatiONs) framework, which aims at delivering the most critical protein interface mutations that can be used to design new protein binders. To realize this aim, PROT-ON takes the 3D coordinates of a protein dimer as an input. Then, it probes all possible interface mutations on the selected protein partner with EvoEF1 or FoldX. The calculated mutational energy landscape is statistically analyzed to find the most enriching and depleting mutations. Afterward, these extreme mutations are filtered out according to stability and optionally according to evolutionary criteria. The final remaining mutation list is presented to the user as the designer mutation set. Together with this set, PROT-ON provides several residue- and energy-based plots, portraying the synthetic energy landscape of the probed mutations. The stand-alone version of PROT-ON is deposited at https://github.com/CSB-KaracaLab/prot-on. The users can also use PROT-ON through our user-friendly web service http://proton.tools.ibg.edu.tr:8001/ (runs with EvoEF1 only). Considering its speed and the range of analysis provided, we believe that PROT-ON presents a promising means to estimate designer mutations.

Evaluation of AlphaFold structure-based protein stability prediction on missense variations in cancer

Identifying pathogenic missense variants in hereditary cancer is critical to the efforts of patient surveillance and risk-reduction strategies. For this purpose, many different gene panels consisting of different number and/or set of genes are available and we are particularly interested in a panel of 26 genes with a varying degree of hereditary cancer risk consisting of ABRAXAS1, ATM, BARD1, BLM, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, EPCAM, MEN1, MLH1, MRE11, MSH2, MSH6, MUTYH, NBN, PALB2, PMS2, PTEN, RAD50, RAD51C, RAD51D, STK11, TP53, and XRCC2. In this study, we have compiled a collection of the missense variations reported in any of these 26 genes. More than a thousand missense variants were collected from ClinVar and the targeted screen of a breast cancer cohort of 355 patients which contributed to this set with 160 novel missense variations. We analyzed the impact of the missense variations on protein stability by five different predictors including both sequence- (SAAF2EC and MUpro) and structure-based (Maestro, mCSM, CUPSAT) predictors. For the structure-based tools, we have utilized the AlphaFold (AF2) protein structures which comprise the first structural analysis of this hereditary cancer proteins. Our results agreed with the recent benchmarks that computed the power of stability predictors in discriminating the pathogenic variants. Overall, we reported a low-to-medium-level performance for the stability predictors in discriminating pathogenic variants, except MUpro which had an AUROC of 0.534 (95% CI [0.499-0.570]). The AUROC values ranged between 0.614-0.719 for the total set and 0.596-0.682 for the set with high AF2 confidence regions. Furthermore, our findings revealed that the confidence score for a given variant in the AF2 structure could alone predict pathogenicity more robustly than any of the tested stability predictors with an AUROC of 0.852. Altogether, this study represents the first structural analysis of the 26 hereditary cancer genes underscoring 1) the thermodynamic stability predicted from AF2 structures as a moderate and 2) the confidence score of AF2 as a strong descriptor for variant pathogenicity.

Polymorphisms in SHISA3 and RFC3 genes and their association with feed conversion ratio in Hu sheep

In animal husbandry, feed efficiency is a crucial economic trait. In this study, the general linear model was used to perform association analysis for various genotypes and feed conversion ratio (FCR)-related traits. Reverse transcription-quantitative PCR (RT-qPCR) was used to detect the expression of SHISA3 and RFC3 mRNA levels in 10 tissues from 6 sheep. The results showed that SNPs in the NC_040257.1:c.625 T > C and NC_040261.1:g.9905 T > C were analyzed whether they were associated to feed efficiency parameters in Hu sheep (body weight, feed intake, average daily growth, and feed conversion ratio). NC_040257.1:c.625 T > C was shown to be significantly associated with body weight at 80, 100, and 120 days as well as feed conversion ratio (P < 0.05), whereas NC_040261.1:g.9905 T > C was found to be significantly associated with average daily weight gain from 80-140 days (ADG80-140) and FCR (P < 0.05). In Hu sheep, the CC genotypes of SHISA3 and RFC3 were the most common genotypes related to feed efficiency traits. Furthermore, the feed conversion ratio of the combined genotypes TT ^SHISA3-CC ^RFC3, TT ^SHISA3-CT ^RFC3, TT ^SHISA3-TT ^RFC3, CT ^SHISA3-CC ^RFC3 and CT ^SHISA3-CT ^RFC3 was significantly better than the FCR of CC ^SHISA3-TT ^RFC3. RT-qPCR results showed that the expression levels of SHISA3 were lower in the lung than in spleen, kidney, muscle and lymph (P < 0.05), and RFC3 was the lung had a highly significant higher expression level than the heart, liver, spleen, and muscle (P < 0.01). In conclusion, SHISA3 and RFC3 polymorphisms can be used as genetic markers for improving feed conversion efficiency in Hu sheep.

Analyses of Mutation Displacements from Homology Models

Evaluation of the structural perturbations introduced by a single amino acid mutation is the main issue for protein structural biology. We propose here to present some recent advances in methods, allowing the splitting of distortion between the actual substitution effect and the contribution of the local flexibility of the position where the mutation occurs. Its main drawback is the need of many structures with a single mutation in each of them. To bypass this difficulty, we propose to use molecular modeling tools, with several software enabling us to build a model from a template, given the sequence. As a proof of concept, we rely on a gold standard, the human lysozyme. Both wild-type and three mutant structures are available in the PDB. Two of these mutations result in amyloid fibril formation, and the last one is neutral. As a conclusion, irrespective of the algorithm used for modeling, side chain conformations at the site of mutation are reliable, although long-range effects are out of reach of these tools.

Computed cancer interactome explains the effects of somatic mutations in cancers

Protein-protein interactions (PPIs) are involved in almost all essential cellular processes. Perturbation of PPI networks plays critical roles in tumorigenesis, cancer progression, and metastasis. While numerous high-throughput experiments have produced a vast amount of data for PPIs, these data sets suffer from high false positive rates and exhibit a high degree of discrepancy. Coevolution of amino acid positions between protein pairs has proven to be useful in identifying interacting proteins and providing structural details of the interaction interfaces with the help of deep learning methods like AlphaFold (AF). In this study, we applied AF to investigate the cancer protein-protein interactome. We predicted 1,798 PPIs for cancer driver proteins involved in diverse cellular processes such as transcription regulation, signal transduction, DNA repair, and cell cycle. We modeled the spatial structures for the predicted binary protein complexes, 1,087 of which lacked previous 3D structure information. Our predictions offer novel structural insight into many cancer-related processes such as the MAP kinase cascade and Fanconi anemia pathway. We further investigated the cancer mutation landscape by mapping somatic missense mutations (SMMs) in cancer to the predicted PPI interfaces and performing enrichment and depletion analyses. Interfaces enriched or depleted with SMMs exhibit different preferences for functional categories. Interfaces enriched in mutations tend to function in pathways that are deregulated in cancers and they may help explain the molecular mechanisms of cancers in patients; interfaces lacking mutations appear to be essential for the survival of cancer cells and thus may be future targets for PPI modulating drugs.

Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure

Most known pathogenic mutations occur in protein-coding regions of DNA and change the way proteins are made. Taking protein structure into account has therefore provided great insight into the molecular mechanisms underlying human genetic disease. While there has been much focus on how mutations can disrupt protein structure and thus cause a loss of function (LOF), alternative mechanisms, specifically dominant-negative (DN) and gain-of-function (GOF) effects, are less understood. Here, we investigate the protein-level effects of pathogenic missense mutations associated with different molecular mechanisms. We observe striking differences between recessive vs dominant, and LOF vs non-LOF mutations, with dominant, non-LOF disease mutations having much milder effects on protein structure, and DN mutations being highly enriched at protein interfaces. We also find that nearly all computational variant effect predictors, even those based solely on sequence conservation, underperform on non-LOF mutations. However, we do show that non-LOF mutations could potentially be identified by their tendency to cluster in three-dimensional space. Overall, our work suggests that many pathogenic mutations that act via DN and GOF mechanisms are likely being missed by current variant prioritisation strategies, but that there is considerable scope to improve computational predictions through consideration of molecular disease mechanisms.

Most known pathogenic mutations occur in protein-coding regions of DNA and change the way proteins are made. Here the authors analyse the locations of thousands of human disease mutations and their predicted effects on protein structure and show that,while loss-of-function mutations tend to be highly disruptive, non-loss-of-function mutations are in general much milder at a protein structural level.

Implications of disease-related mutations at protein-protein interfaces

Protein-protein interfaces have been attracting great attention owing to their critical roles in protein-protein interactions and the fact that human disease-related mutations are generally enriched in them. Recently, substantial research progress has been made in this field, which has significantly promoted the understanding and treatment of various human diseases. For example, many studies have discovered the properties of disease-related mutations. Besides, as more large-scale experimental data become available, various computational approaches have been proposed to advance our understanding of disease mutations from the data. Here, we overview recent advances in characteristics of disease-related mutations at protein-protein interfaces, mutation effects on protein interactions, and investigation of mutations on specific diseases.

The properties of human disease mutations at protein interfaces

The assembly of proteins into complexes and their interactions with other biomolecules are often vital for their biological function. While it is known that mutations at protein interfaces have a high potential to be damaging and cause human genetic disease, there has been relatively little consideration for how this varies between different types of interfaces. Here we investigate the properties of human pathogenic and putatively benign missense variants at homomeric (isologous and heterologous), heteromeric, DNA, RNA and other ligand interfaces, and at different regions in proteins with respect to those interfaces. We find that different types of interfaces vary greatly in their propensity to be associated with pathogenic mutations, with homomeric heterologous and DNA interfaces being particularly enriched in disease. We also find that residues that do not directly participate in an interface, but are close in three-dimensional space, show a significant disease enrichment. Finally, we observe that mutations at different types of interfaces tend to have distinct property changes when undergoing amino acid substitutions associated with disease, and that this is linked to substantial variability in their identification by computational variant effect predictors.

Nearly all proteins interact with other molecules as part of their biological function. For example, proteins can interact with other copies of the same type of protein, with different proteins, with DNA, or with small ligand molecules. Many mutations at protein interfaces, the regions of proteins that interact with other molecules, are known to cause human genetic disease. In this study, we first investigate how different types of protein interfaces have different tendencies to be associated with disease. We also show that the closer a mutation is to an interface, the more likely it is to cause disease. Finally, we study how mutations at different types of interfaces tend to be associated with different changes in amino acid properties, which appears to influence our ability to computationally predict the effects of mutations. Ultimately, we hope that consideration of protein interface properties will eventually improve our ability to identify new disease-causing mutations.

Probing altered enzyme activity in the biochemical characterization of cancer

Enzymes have evolved to catalyze their precise reactions at the necessary rates, locations, and time to facilitate our development, to respond to a variety of insults and challenges, and to maintain a healthy, balanced state. Enzymes achieve this extraordinary feat through their unique kinetic parameters, myriad regulatory strategies, and their sensitivity to their surroundings, including substrate concentration and pH. The Cancer Genome Atlas (TCGA) highlights the extraordinary number of ways in which the finely tuned activities of enzymes can be disrupted, contributing to cancer development and progression often due to somatic and/or inherited genetic alterations. Rather than being limited to the domain of enzymologists, kinetic constants such as k_cat, K_m, and k_cat/K_m are highly informative parameters that can impact a cancer patient in tangible ways—these parameters can be used to sort tumor driver mutations from passenger mutations, to establish the pathways that cancer cells rely on to drive patients’ tumors, to evaluate the selectivity and efficacy of anti-cancer drugs, to identify mechanisms of resistance to treatment, and more. In this review, we will discuss how changes in enzyme activity, primarily through somatic mutation, can lead to altered kinetic parameters, new activities, or changes in conformation and oligomerization. We will also address how changes in the tumor microenvironment can affect enzymatic activity, and briefly describe how enzymology, when combined with additional powerful tools, and can provide us with tremendous insight into the chemical and molecular mechanisms of cancer.

Structural exploration with AlphaFold2-generated STAT3α structure reveals selective elements in STAT3α-GRIM-19 interactions involved in negative regulation

STAT3, an important transcription factor constitutively activated in cancers, is bound specifically by GRIM-19 and this interaction inhibits STAT3-dependent gene expression. GRIM-19 is therefore, considered as an inhibitor of STAT3 and may be an effective anti-cancer therapeutic target. While STAT3 exists in a dimeric form in the cytoplasm and nucleus, it is mostly present in a monomeric form in the mitochondria. Although GRIM-19-binding domains of STAT3 have been identified in independent experiments, yet the identified domains are not the same, and hence, discrepancies exist. Human STAT3-GRIM-19 complex has not been crystallised yet. Dictated by fundamental biophysical principles, the binding region, interactions and effects of hotspot mutations can provide us a clue to the negative regulatory mechanisms of GRIM-19. Prompted by the very nature of STAT3 being a challenging molecule, and to understand the structural basis of binding and interactions in STAT3α-GRIM-19 complex, we performed homology modelling and ab-initio modelling with evolutionary information using I-TASSER and avant-garde AlphaFold2, respectively, to generate monomeric, and subsequently, dimeric STAT3α structures. The dimeric form of STAT3α structure was observed to potentially exist in an anti-parallel orientation of monomers. We demonstrate that during the interactions with both unphosphorylated and phosphorylated STAT3α, the NTD of GRIM-19 binds most strongly to the NTD of STAT3α, in direct contrast to the earlier works. Key arginine residues at positions 57, 58 and 68 of GRIM-19 are mainly involved in the hydrogen-bonded interactions. An intriguing feature of these arginine residues is that these display a consistent interaction pattern across unphosphorylated and phosphorylated monomers as well as unphosphorylated dimers in STAT3α-GRIM-19 complexes. MD studies verified the stability of these complexes. Analysing the binding affinity and stability through free energy changes upon mutation, we found GRIM-19 mutations Y33P and Q61L and among GRIM-19 arginines, R68P and R57M, to be one of the top-most major and minor disruptors of binding, respectively. The proportionate increase in average change in binding affinity upon mutation was inclined more towards GRIM-19 mutants, leading to the surmise that GRIM-19 may play a greater role in the complex formation. These studies propound a novel structural perspective of STAT3α-GRIM-19 binding and inhibitory mechanisms in both the monomeric and dimeric forms of STAT3α as compared to that observed from the earlier experiments, these experimental observations being inconsistent among each other.

In-silico identification of frequently mutated genes and their co-enriched metabolic pathways associated with Prostate cancer progression

Prostate cancer (PCa) has emerged as a significant health burden in men globally. Several genetic anomalies such as mutations and also epigenetic aberrations are responsible for the heterogeneity of this disease. This study identified the 20 most frequently mutated genes reported in PCa based on literature and database survey. Further gene ontology and functional enrichment analysis were conducted to determine their co-modulated molecular and biological pathways. A protein-protein interaction network was used for the identification of hub genes. These hub genes identified were then subjected to survival analysis. The prognostic values of these identified genes were investigated using GEPIA and HPA. Gene Ontology analysis of the identified genes depicted that these genes significantly contributed to the cell cycle, apoptosis, angiogenesis and TGF-β receptor signalling. Further, the research showed that high expressions of identified mutated genes led to a reduction in the long-term survival of PCa patients, which was supported by immunohistochemical and mRNA expression level data. Our results suggest that identified panel of mutated genes viz., CTNNB1, TP53, ATM, AR and KMT2D play crucial roles in the onset and progression of PCa, thereby providing candidate diagnostic markers for PCa for individualised treatment in the future.

Computational Assessment of Protein-Protein Binding Affinity by Reverse Engineering the Energetics in Protein Complexes

The cellular functions of proteins are maintained by forming diverse complexes. The stability of these complexes is quantified by the measurement of binding affinity, and mutations that alter the binding affinity can cause various diseases such as cancer and diabetes. As a result, accurate estimation of the binding stability and the effects of mutations on changes of binding affinity is a crucial step to understanding the biological functions of proteins and their dysfunctional consequences. It has been hypothesized that the stability of a protein complex is dependent not only on the residues at its binding interface by pairwise interactions but also on all other remaining residues that do not appear at the binding interface. Here, we computationally reconstruct the binding affinity by decomposing it into the contributions of interfacial residues and other non-interfacial residues in a protein complex. We further assume that the contributions of both interfacial and non-interfacial residues to the binding affinity depend on their local structural environments such as solvent-accessible surfaces and secondary structural types. The weights of all corresponding parameters are optimized by Monte-Carlo simulations. After cross-validation against a large-scale dataset, we show that the model not only shows a strong correlation between the absolute values of the experimental and calculated binding affinities, but can also be an effective approach to predict the relative changes of binding affinity from mutations. Moreover, we have found that the optimized weights of many parameters can capture the first-principle chemical and physical features of molecular recognition, therefore reversely engineering the energetics of protein complexes. These results suggest that our method can serve as a useful addition to current computational approaches for predicting binding affinity and understanding the molecular mechanism of protein–protein interactions.

SMAD4 mutations identified in Iranian patients with colorectal cancer and polyp

AIM

Search for SMAD4 mutations in Colorectal cancer (CRC) or polyp in Iran.

BACKGROUND

Colorectal cancer is one of the five prevalent cancers among the Iranian population; however, its molecular mechanisms are not fully understood. The vast majority of CRCs arise from neoplastic polyp.

METHODS

Colorectal cancer and polyp lesions with matched normal tissues from patients who had undergone colonoscopy in Taleghani Hospital (January 2009 - November 2010) were included in the study. DNA extraction and PCR-sequencing for exons 5-11 of the SMAD-4 gene were carried out on 39 and 30 specimens of polyp and adenocarcinoma, respectively.

RESULTS

Of cancer and polyp specimens, 33.3% and 28.2%, respectively, were mutated in the Smad-4 gene. The majority of SMAD4 mutations, especially in the MH₂ domain were missense mutations (63.6% and 68.75, respectively). In cancer, codon 435 and in polyp, codons 435 and 399 were the most common alterations. Unlike cancer specimens, transversion was found frequently in the polyp (56.25% vs. 35.7%). CG>TA transition was about 18.75% and 14.3% in cancer and polyp samples, respectively. Mutations of codon 264 and C.483-4 were seen both in cancer and neoplastic polyps.

CONCLUSION

As frequent alterations, missense mutations are presumably selected during tumorigenesis and polyposis due to their structural impacts on SMAD4 functions and TGF-ß signaling pathway. The lower frequency of CG>TA can be attributed to global genome hypomethylation. Presumably, SMAD4 mutations had occurred in the primary polyps, and some of these mutated cells then developed into carcinoma. On the other hand, polyp-specific mutations may lower the risk of CRC.

Identification of pathogenic missense mutations using protein stability predictors

Attempts at using protein structures to identify disease-causing mutations have been dominated by the idea that most pathogenic mutations are disruptive at a structural level. Therefore, computational stability predictors, which assess whether a mutation is likely to be stabilising or destabilising to protein structure, have been commonly used when evaluating new candidate disease variants, despite not having been developed specifically for this purpose. We therefore tested 13 different stability predictors for their ability to discriminate between pathogenic and putatively benign missense variants. We find that one method, FoldX, significantly outperforms all other predictors in the identification of disease variants. Moreover, we demonstrate that employing predicted absolute energy change scores improves performance of nearly all predictors in distinguishing pathogenic from benign variants. Importantly, however, we observe that the utility of computational stability predictors is highly heterogeneous across different proteins, and that they are all inferior to the best performing variant effect predictors for identifying pathogenic mutations. We suggest that this is largely due to alternate molecular mechanisms other than protein destabilisation underlying many pathogenic mutations. Thus, better ways of incorporating protein structural information and molecular mechanisms into computational variant effect predictors will be required for improved disease variant prioritisation.

PremPRI: Predicting the Effects of Missense Mutations on Protein-RNA Interactions

Protein–RNA interactions are crucial for many cellular processes, such as protein synthesis and regulation of gene expression. Missense mutations that alter protein–RNA interaction may contribute to the pathogenesis of many diseases. Here, we introduce a new computational method PremPRI, which predicts the effects of single mutations occurring in RNA binding proteins on the protein–RNA interactions by calculating the binding affinity changes quantitatively. The multiple linear regression scoring function of PremPRI is composed of three sequence- and eight structure-based features, and is parameterized on 248 mutations from 50 protein–RNA complexes. Our model shows a good agreement between calculated and experimental values of binding affinity changes with a Pearson correlation coefficient of 0.72 and the corresponding root-mean-square error of 0.76 kcal·mol⁻¹, outperforming three other available methods. PremPRI can be used for finding functionally important variants, understanding the molecular mechanisms, and designing new protein–RNA interaction inhibitors.

Protein-Protein Interactions Mediated by Intrinsically Disordered Protein Regions Are Enriched in Missense Mutations

Because proteins are fundamental to most biological processes, many genetic diseases can be traced back to single nucleotide variants (SNVs) that cause changes in protein sequences. However, not all SNVs that result in amino acid substitutions cause disease as each residue is under different structural and functional constraints. Influential studies have shown that protein–protein interaction interfaces are enriched in disease-associated SNVs and depleted in SNVs that are common in the general population. These studies focus primarily on folded (globular) protein domains and overlook the prevalent class of protein interactions mediated by intrinsically disordered regions (IDRs). Therefore, we investigated the enrichment patterns of missense mutation-causing SNVs that are associated with disease and cancer, as well as those present in the healthy population, in structures of IDR-mediated interactions with comparisons to classical globular interactions. When comparing the different categories of interaction interfaces, division of the interface regions into solvent-exposed rim residues and buried core residues reveal distinctive enrichment patterns for the various types of missense mutations. Most notably, we demonstrate a strong enrichment at the interface core of interacting IDRs in disease mutations and its depletion in neutral ones, which supports the view that the disruption of IDR interactions is a mechanism underlying many diseases. Intriguingly, we also found an asymmetry across the IDR interaction interface in the enrichment of certain missense mutation types, which may hint at an increased variant tolerance and urges further investigations of IDR interactions.

Ab-initio binding of barnase–barstar with DelPhiForce steered Molecular Dynamics (DFMD) approach

Receptor–ligand interactions are involved in various biological processes, therefore understanding the binding mechanism and ability to predict the binding mode are essential for many biological investigations. While many computational methods exist to predict the 3D structure of the corresponding complex provided the knowledge of the monomers, here we use the newly developed DelPhiForce steered Molecular Dynamics (DFMD) approach to model the binding of barstar to barnase to demonstrate that first-principles methods are also capable of modeling the binding. Essential component of DFMD approach is enhancing the role of long-range electrostatic interactions to provide guiding force of the monomers toward their correct binding orientation and position. Thus, it is demonstrated that the DFMD can successfully dock barstar to barnase even if the initial positions and orientations of both are completely different from the correct ones. Thus, the electrostatics provides orientational guidance along with pulling force to deliver the ligand in close proximity to the receptor.

PertInInt: An Integrative, Analytical Approach to Rapidly Uncover Cancer Driver Genes with Perturbed Interactions and Functionalities

A major challenge in cancer genomics is to identify genes with functional roles in cancer and uncover their mechanisms of action. We introduce an integrative framework that identifies cancer-relevant genes by pinpointing those whose interaction or other functional sites are enriched in somatic mutations across tumors. We derive analytical calculations that enable us to avoid time-prohibitive permutation-based significance tests, making it computationally feasible to simultaneously consider multiple measures of protein site functionality. Our accompanying software, PertInInt, combines knowledge about sites participating in interactions with DNA, RNA, peptides, ions, or small molecules with domain, evolutionary conservation, and gene-level mutation data. When applied to 10,037 tumor samples, PertInInt uncovers both known and newly predicted cancer genes, while additionally revealing what types of interactions or other functionalities are disrupted. PertInInt’s analysis demonstrates that somatic mutations are frequently enriched in interaction sites and domains and implicates interaction perturbation as a pervasive cancer-driving event.

A fast, analytical framework called PertInInt enables efficient integration of multiple measures of protein site functionality—including interaction, domain, and evolutionary conservation—with gene-level mutation data in order to rapidly detect cancer driver genes along with their disrupted functionalities.

SAAMBE-3D: Predicting Effect of Mutations on Protein-Protein Interactions

Maintaining wild type protein-protein interactions is essential for the normal function of cell and any mutation that alter their characteristics can cause disease. Therefore, the ability to correctly and quickly predict the effect of amino acid mutations is crucial for understanding disease effects and to be able to carry out genome-wide studies. Here, we report a new development of the SAAMBE method, SAAMBE-3D, which is a machine learning-based approach, resulting in accurate predictions and is extremely fast. It achieves the Pearson correlation coefficient ranging from 0.78 to 0.82 depending on the training protocol in benchmarking five-fold validation test against the SKEMPI v2.0 database and outperforms currently existing algorithms on various blind-tests. Furthermore, optimized and tested via five-fold cross-validation on the Cornell University dataset, the SAAMBE-3D achieves AUC of 1.0 and 0.96 on a homo and hereto-dimer test datasets. Another important feature of SAAMBE-3D is that it is very fast, it takes less than a fraction of a second to complete a prediction. SAAMBE-3D is available as a web server and as well as a stand-alone code, the last one being another important feature allowing other researchers to directly download the code and run it on their local computer. Combined all together, SAAMBE-3D is an accurate and fast software applicable for genome-wide studies to assess the effect of amino acid mutations on protein-protein interactions. The webserver and the stand-alone codes (SAAMBE-3D for predicting the change of binding free energy and SAAMBE-3D-DN for predicting if the mutation is disruptive or non-disruptive) are available.

Edgetic perturbation signatures represent known and novel cancer biomarkers

Isoform switching is a recently characterized hallmark of cancer, and often translates to the loss or gain of domains mediating protein interactions and thus, the re-wiring of the interactome. Recent computational tools leverage domain-domain interaction data to resolve the condition-specific interaction networks from RNA-Seq data accounting for the domain content of the primary transcripts expressed. Here, we used The Cancer Genome Atlas RNA-Seq datasets to generate 642 patient-specific pairs of interactomes corresponding to both the tumor and the healthy tissues across 13 cancer types. The comparison of these interactomes provided a list of patient-specific edgetic perturbations of the interactomes associated with the cancerous state. We found that among the identified perturbations, select sets are robustly shared between patients at the multi-cancer, cancer-specific and cancer sub-type specific levels. Interestingly, the majority of the alterations do not directly involve significantly mutated genes, nevertheless, they strongly correlate with patient survival. The findings (available at EdgeExplorer: “http://webclu.bio.wzw.tum.de/EdgeExplorer”) are a new source of potential biomarkers for classifying cancer types and the proteins we identified are potential anti-cancer therapy targets.

MutaBind2: Predicting the Impacts of Single and Multiple Mutations on Protein-Protein Interactions

Missense mutations may affect proteostasis by destabilizing or over-stabilizing protein complexes and changing the pathway flux. Predicting the effects of stabilizing mutations on protein-protein interactions is notoriously difficult because existing experimental sets are skewed toward mutations reducing protein-protein binding affinity and many computational methods fail to correctly evaluate their effects. To address this issue, we developed a method MutaBind2, which estimates the impacts of single as well as multiple mutations on protein-protein interactions. MutaBind2 employs only seven features, and the most important of them describe interactions of proteins with the solvent, evolutionary conservation of the site, and thermodynamic stability of the complex and each monomer. This approach shows a distinct improvement especially in evaluating the effects of mutations increasing binding affinity. MutaBind2 can be used for finding disease driver mutations, designing stable protein complexes, and discovering new protein-protein interaction inhibitors.

Computational assessment of SKA1 as a potential cancer biomarker

Background

Spindle and kinetochore associated complex subunit 1 (SKA1) is an essential component of SKA complex, which is required for the proper formation of kinetochore–microtubule attachment and timely mitotic progression. The aim of this study is to perform detailed analyses of SKA1 genomic and expression alterations in cancers and to assess SKA1 as a biomarker for predicting human cancers and patient prognosis.

Materials and methods

Missense mutations from human cancers were extracted, deleterious missense mutations were predicted and shown on 3D SKA1 protein. SKA1 expression and the effect of SKA1 expression on patient survival were investigated in human cancers.

Results and discussion

Most of the predicted deleterious mutations were detected on microtubule-binding domain of SKA1, suggesting mutations on microtubule-binding domain might be more relevant in human cancers. High SKA1 expression was detected in various cancers. In addition, patients with high SKA1 expression showed poor overall survival compared to patients with low SKA1 expression in breast, lung and gastric cancers.

Conclusion

These results suggest that high SKA1 expression might be a prognostic and predictive biomarker for several cancers and mainly mutations in the microtubule-binding domain of SKA1 might have a deleterious effect for SKA1.

Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when

The increasing ease with which massive genetic information can be obtained from patients or healthy individuals has stimulated the development of interpretive bioinformatics tools as aids in clinical practice. Most such tools analyze evolutionary information and simple physical-chemical properties to predict whether replacement of one amino acid residue with another will be tolerated or cause disease. Those approaches achieve up to 80-85% accuracy as binary classifiers (neutral/pathogenic). As such accuracy is insufficient for medical decision to be based on, and it does not appear to be increasing, more precise methods, such as full-atom molecular dynamics (MD) simulations in explicit solvent, are also discussed. Then, to describe the goal of interpreting human genetic variations at large scale through MD simulations, we restrictively refer to all possible protein variants carrying single-amino-acid substitutions arising from single-nucleotide variations as the human variome. We calculate its size and develop a simple model that allows calculating the simulation time needed to have a 0.99 probability of observing unfolding events of any unstable variant. The knowledge of that time enables performing a binary classification of the variants (stable-potentially neutral/unstable-pathogenic). Our model indicates that the human variome cannot be simulated with present computing capabilities. However, if they continue to increase as per Moore's law, it could be simulated (at 65°C) spending only 3 years in the task if we started in 2031. The simulation of individual protein variomes is achievable in short times starting at present. International coordination seems appropriate to embark upon massive MD simulations of protein variants.

In silico structural and functional characterization and phylogenetic study of alkaline phosphatase in bacterium, Rhizobium leguminosarum (Frank 1879)

Phosphorus is one of the primary macronutrient of plants, which is present in soil. It is essential for normal growth and development of plants. Plants use inorganic form of phosphate but organic form can also be assimilated with the help of soil inhabiting bacteria. Alkaline phosphatase is an enzyme present in Rizobium bacteria. This enzyme is responsible for solubilization and mineralization of organic phosphate and makes it readily available for plants. In the present study, nine different strains of Rhizobium leguminosarum were selected for a detailed computational structural and functional characterization and phylogenetic studies of alkaline phosphatase. Amino acid sequences were retrieved from UniProt and saved in FASTA format for use in analysis. Phylogenetic analysis of these strains was done by using MEGA7. 3D structure prediction was performed by using online server I-Tasser. Galaxy Web and 3D Refine were used for structure refinement. The refined structures were evaluated using two validation servers, QMEAN and SAVES. Protein-protein interaction analysis was done by using STRING. For detailed functional characterization, Cofactor, Coach, RaptorX, PSORT and MEME were used. Overall quality of predicted protein models was above 80%. Refined and validated models were submitted into PMDB. Seven out of nine strains were closely related and other two were distantly related. Protein-Protein interaction showed no significant co-expression among the interaction partners.

Analysis of single amino acid variations in singlet hot spots of protein-protein interfaces

Motivation

Single amino acid variations (SAVs) in protein-protein interaction (PPI) sites play critical roles in diseases. PPI sites (interfaces) have a small subset of residues called hot spots that contribute significantly to the binding energy, and they may form clusters called hot regions. Singlet hot spots are the single amino acid hot spots outside of the hot regions. The distribution of SAVs on the interface residues may be related to their disease association.

Results

We performed statistical and structural analyses of SAVs with literature curated experimental thermodynamics data, and demonstrated that SAVs which destabilize PPIs are more likely to be found in singlet hot spots rather than hot regions and energetically less important interface residues. In contrast, non-hot spot residues are significantly enriched in neutral SAVs, which do not affect PPI stability. Surprisingly, we observed that singlet hot spots tend to be enriched in disease-causing SAVs, while benign SAVs significantly occur in non-hot spot residues. Our work demonstrates that SAVs in singlet hot spot residues have significant effect on protein stability and function.

Availability and implementation

The dataset used in this paper is available as Supplementary Material. The data can be found at http://prism.ccbb.ku.edu.tr/data/sav/ as well.

Supplementary information

Supplementary data are available at Bioinformatics online.

3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients

Glioblastoma multiforme (GBM) is the most aggressive type of brain tumor. Molecular heterogeneity is a hallmark of GBM tumors that is a barrier in developing treatment strategies. In this study, we used the nonsynonymous mutations of GBM tumors deposited in The Cancer Genome Atlas (TCGA) and applied a systems level approach based on biophysical characteristics of mutations and their organization in patient-specific subnetworks to reduce inter-patient heterogeneity and to gain potential clinically relevant insights. Approximately 10% of the mutations are located in "patches" which are defined as the set of residues spatially in close proximity that are mutated across multiple patients. Grouping mutations as 3D patches reduces the heterogeneity across patients. There are multiple patches that are relatively small in oncogenes, whereas there are a small number of very large patches in tumor suppressors. Additionally, different patches in the same protein are often located at different domains that can mediate different functions. We stratified the patients into five groups based on their potentially affected pathways that are revealed from the patient-specific subnetworks. These subnetworks were constructed by integrating mutation profiles of the patients with the interactome data. Network-guided clustering showed significant association between the groups and patient survival (P-value = 0.0408). Also, each group carries a set of signature 3D mutation patches that affect predominant pathways. We integrated drug sensitivity data of GBM cell lines with the mutation patches and the patient groups to analyze the possible therapeutic outcome of these patches. We found that Pazopanib might be effective in Group 3 by targeting CSF1R. Additionally, inhibiting ATM that is a mediator of PTEN phosphorylation may be ineffective in Group 2. We believe that from mutations to networks and eventually to clinical and therapeutic data, this study provides a novel perspective in the network-guided precision medicine.

BioStruct-Africa: empowering Africa-based scientists through structural biology knowledge transfer and mentoring - recent advances and future perspectives

Being able to visualize biology at the molecular level is essential for our understanding of the world. A structural biology approach reveals the molecular basis of disease processes and can guide the design of new drugs as well as aid in the optimization of existing medicines. However, due to the lack of a synchrotron light source, adequate infrastructure, skilled persons and incentives for scientists in addition to limited financial support, the majority of countries across the African continent do not conduct structural biology research. Nevertheless, with technological advances such as robotic protein crystallization and remote data collection capabilities offered by many synchrotron light sources, X-ray crystallography is now potentially accessible to Africa-based scientists. This leap in technology led to the establishment in 2017 of BioStruct-Africa, a non-profit organization (Swedish corporate ID: 802509-6689) whose core aim is capacity building for African students and researchers in the field of structural biology with a focus on prevalent diseases in the African continent. The team is mainly composed of, but not limited to, a group of structural biologists from the African diaspora. The members of BioStruct-Africa have taken up the mantle to serve as a catalyst in order to facilitate the information and technology transfer to those with the greatest desire and need within Africa. BioStruct-Africa achieves this by organizing workshops onsite at our partner universities and institutions based in Africa, followed by post-hoc online mentoring of participants to ensure sustainable capacity building. The workshops provide a theoretical background on protein crystallography, hands-on practical experience in protein crystallization, crystal harvesting and cryo-cooling, live remote data collection on a synchrotron beamline, but most importantly the links to drive further collaboration through research. Capacity building for Africa-based researchers in structural biology is crucial to win the fight against the neglected tropical diseases, e.g. ascariasis, hookworm, trichuriasis, lymphatic filariasis, active trachoma, loiasis, yellow fever, leprosy, rabies, sleeping sickness, onchocerciasis, schistosomiasis, etc., that constitute significant health, social and economic burdens to the continent. BioStruct-Africa aims to build local and national expertise that will have direct benefits for healthcare within the continent.

Cyclin-CDK Complexes are Key Controllers of Capacitation-Dependent Actin Dynamics in Mammalian Spermatozoa

Mammalian spermatozoa are infertile immediately after ejaculation and need to undergo a functional maturation process to acquire the competence to fertilize the female egg. During this process, called capacitation, the actin cytoskeleton dramatically changes its organization. First, actin fibers polymerize, forming a network over the anterior part of the sperm cells head, and then it rapidly depolymerizes and disappears during the exocytosis of the acrosome content (the acrosome reaction (AR)). Here, we developed a computational model representing the actin dynamics (AD) process on mature spermatozoa. In particular, we represented all the molecular events known to be involved in AD as a network of nodes linked by edges (the interactions). After the network enrichment, using an online resource (STRING), we carried out the statistical analysis on its topology, identifying the controllers of the system and validating them in an experiment of targeted versus random attack to the network. Interestingly, among them, we found that cyclin-dependent kinase (cyclin–CDK) complexes are acting as stronger controllers. This finding is of great interest since it suggests the key role that cyclin–CDK complexes could play in controlling AD during sperm capacitation, leading us to propose a new and interesting non-genomic role for these molecules.

Understanding the impacts of missense mutations on structures and functions of human cancer-related genes: A preliminary computational analysis of the COSMIC Cancer Gene Census

Genomics and genome screening are proving central to the study of cancer. However, a good appreciation of the protein structures coded by cancer genes is also invaluable, especially for the understanding of functions, for assessing ligandability of potential targets, and for designing new drugs. To complement the wealth of information on the genetics of cancer in COSMIC, the most comprehensive database for cancer somatic mutations available, structural information obtained experimentally has been brought together recently in COSMIC-3D. Even where structural information is available for a gene in the Cancer Gene Census, a list of genes in COSMIC with substantial evidence supporting their impacts in cancer, this information is quite often for a single domain in a larger protein or for a single protomer in a multiprotein assembly. Here, we show that over 60% of the genes included in the Cancer Gene Census are predicted to possess multiple domains. Many are also multicomponent and membrane-associated molecular assemblies, with mutations recorded in COSMIC affecting such assemblies. However, only 469 of the gene products have a structure represented in the PDB, and of these only 87 structures have 90-100% coverage over the sequence and 69 have less than 10% coverage. As a first step to bridging gaps in our knowledge in the many cases where individual protein structures and domains are lacking, we discuss our attempts of protein structure modelling using our pipeline and investigating the effects of mutations using two of our in-house methods (SDM2 and mCSM) and identifying potential driver mutations. This allows us to begin to understand the effects of mutations not only on protein stability but also on protein-protein, protein-ligand and protein-nucleic acid interactions. In addition, we consider ways to combine the structural information with the wealth of mutation data available in COSMIC. We discuss the impacts of COSMIC missense mutations on protein structure in order to identify and assess the molecular consequences of cancer-driving mutations.

Mutational signatures and mutable motifs in cancer genomes

Cancer is a genetic disorder, meaning that a plethora of different mutations, whether somatic or germ line, underlie the etiology of the 'Emperor of Maladies'. Point mutations, chromosomal rearrangements and copy number changes, whether they have occurred spontaneously in predisposed individuals or have been induced by intrinsic or extrinsic (environmental) mutagens, lead to the activation of oncogenes and inactivation of tumor suppressor genes, thereby promoting malignancy. This scenario has now been recognized and experimentally confirmed in a wide range of different contexts. Over the past decade, a surge in available sequencing technologies has allowed the sequencing of whole genomes from liquid malignancies and solid tumors belonging to different types and stages of cancer, giving birth to the new field of cancer genomics. One of the most striking discoveries has been that cancer genomes are highly enriched with mutations of specific kinds. It has been suggested that these mutations can be classified into 'families' based on their mutational signatures. A mutational signature may be regarded as a type of base substitution (e.g. C:G to T:A) within a particular context of neighboring nucleotide sequence (the bases upstream and/or downstream of the mutation). These mutational signatures, supplemented by mutable motifs (a wider mutational context), promise to help us to understand the nature of the mutational processes that operate during tumor evolution because they represent the footprints of interactions between DNA, mutagens and the enzymes of the repair/replication/modification pathways.

Protein ensembles link genotype to phenotype

Classically, phenotype is what is observed, and genotype is the genetic makeup. Statistical studies aim to project phenotypic likelihoods of genotypic patterns. The traditional genotype-to-phenotype theory embraces the view that the encoded protein shape together with gene expression level largely determines the resulting phenotypic trait. Here, we point out that the molecular biology revolution at the turn of the century explained that the gene encodes not one but ensembles of conformations, which in turn spell all possible gene-associated phenotypes. The significance of a dynamic ensemble view is in understanding the linkage between genetic change and the gained observable physical or biochemical characteristics. Thus, despite the transformative shift in our understanding of the basis of protein structure and function, the literature still commonly relates to the classical genotype-phenotype paradigm. This is important because an ensemble view clarifies how even seemingly small genetic alterations can lead to pleiotropic traits in adaptive evolution and in disease, why cellular pathways can be modified in monogenic and polygenic traits, and how the environment may tweak protein function.

Pathogenicity and functional impact of non-frameshifting insertion/deletion variation in the human genome

Differentiation between phenotypically neutral and disease-causing genetic variation remains an open and relevant problem. Among different types of variation, non-frameshifting insertions and deletions (indels) represent an understudied group with widespread phenotypic consequences. To address this challenge, we present a machine learning method, MutPred-Indel, that predicts pathogenicity and identifies types of functional residues impacted by non-frameshifting insertion/deletion variation. The model shows good predictive performance as well as the ability to identify impacted structural and functional residues including secondary structure, intrinsic disorder, metal and macromolecular binding, post-translational modifications, allosteric sites, and catalytic residues. We identify structural and functional mechanisms impacted preferentially by germline variation from the Human Gene Mutation Database, recurrent somatic variation from COSMIC in the context of different cancers, as well as de novo variants from families with autism spectrum disorder. Further, the distributions of pathogenicity prediction scores generated by MutPred-Indel are shown to differentiate highly recurrent from non-recurrent somatic variation. Collectively, we present a framework to facilitate the interrogation of both pathogenicity and the functional effects of non-frameshifting insertion/deletion variants. The MutPred-Indel webserver is available at http://mutpred.mutdb.org/.