Prediction Of Mutation Effects Research Articles

Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering

Mutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in biotechnology and biomedicine, but remains a fundamental challenge. To resolve this challenge, here we present Protein Mutational Effect Predictor (ProMEP), a general and multiple sequence alignment-free method that enables zero-shot prediction of mutation effects. A multimodal deep representation learning model embedded in ProMEP was developed to comprehensively learn both sequence and structure contexts from ~160 million proteins. ProMEP achieves state-of-the-art performance in mutational effect prediction and accomplishes a tremendous improvement in speed, enabling efficient and intelligent protein engineering. Specifically, ProMEP accurately forecasts mutational consequences on the gene-editing enzymes TnpB and TadA, and successfully guides the development of high-performance gene-editing tools with their engineered variants. The gene-editing efficiency of a 5-site mutant of TnpB reaches up to 74.04% (vs 24.66% for the wild type); and the base editing tool developed on the basis of a TadA 15-site mutant (in addition to the A106V/D108N double mutation that renders deoxyadenosine deaminase activity to TadA) exhibits an A-to-G conversion frequency of up to 77.27% (vs 69.80% for ABE8e, a previous TadA-based adenine base editor) with significantly reduced bystander and off-target effects compared to ABE8e. ProMEP not only showcases superior performance in predicting mutational effects on proteins but also demonstrates a great capability to guide protein engineering. Therefore, ProMEP enables efficient exploration of the gigantic protein space and facilitates practical design of proteins, thereby advancing studies in biomedicine and synthetic biology.

Simultaneous enhancement of multiple functional properties using evolution-informed protein design

A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Altering several properties likely necessitates numerous primary sequence changes, and novel methods are needed to accurately predict combinations of mutations that maintain or enhance function. Models of sequence co-variation (e.g., EVcouplings), which leverage extensive information about various protein properties and activities from homologous protein sequences, have proven effective for many applications including structure determination and mutation effect prediction. We apply EVcouplings to computationally design variants of the model protein TEM-1 β-lactamase. Nearly all the 14 experimentally characterized designs were functional, including one with 84 mutations from the nearest natural homolog. The designs also had large increases in thermostability, increased activity on multiple substrates, and nearly identical structure to the wild type enzyme. This study highlights the efficacy of evolutionary models in guiding large sequence alterations to generate functional diversity for protein design applications.

An Efficient Approach to the Accurate Prediction of Mutational Effects in Antigen Binding to the MHC1.

The major histocompatibility complex (MHC) can recognize and bind to external peptides to generate effective immune responses by presenting the peptides to T cells. Therefore, understanding the binding modes of peptide-MHC complexes (pMHC) and predicting the binding affinity of pMHCs play a crucial role in the rational design of peptide vaccines. In this study, we employed molecular dynamics (MD) simulations and free energy calculations with an Alanine Scanning with Generalized Born and Interaction Entropy (ASGBIE) method to investigate the protein-peptide interaction between HLA-A*02:01 and the G9209 peptide derived from the melanoma antigen gp100. The energy contribution of individual residue was calculated using alanine scanning, and hotspots on both the MHC and the peptides were identified. Our study shows that the pMHC binding is dominated by the van der Waals interactions. Furthermore, we optimized the ASGBIE method, achieving a Pearson correlation coefficient of 0.91 between predicted and experimental binding affinity for mutated antigens. This represents a significant improvement over the conventional MM/GBSA method, which yields a Pearson correlation coefficient of 0.22. The computational protocol developed in this study can be applied to the computational screening of antigens for the MHC1 as well as other protein-peptide binding systems.

Zero-shot mutation effect prediction on protein stability and function using RoseTTAFold.

Predicting the effects of mutations on protein function and stability is an outstanding challenge. Here, we assess the performance of a variant of RoseTTAFold jointly trained for sequence and structure recovery, RFjoint , for mutation effect prediction. Without any further training, we achieve comparable accuracy in predicting mutation effects for a diverse set of protein families using RFjoint to both another zero-shot model (MSA Transformer) and a model that requires specific training on a particular protein family for mutation effect prediction (DeepSequence). Thus, although the architecture of RFjoint was developed to address the protein design problem of scaffolding functional motifs, RFjoint acquired an understanding of the mutational landscapes of proteins during model training that is equivalent to that of recently developed large protein language models. The ability to simultaneously reason over protein structure and sequence could enable even more precise mutation effect predictions following supervised training on the task. These results suggest that RFjoint has a quite broad understanding of protein sequence-structure landscapes, and can be viewed as a joint model for protein sequence and structure which could be broadly useful for protein modeling.

Oncogenic mutations in the DNA-binding domain of FOXO1 that disrupt folding: Quantitative insights from experiments and molecular simulation.

FOXO1, a member of the family of winged-helix motif Forkhead box (FOX) transcription factors, is the most abundantly expressed FOXO member in mature B cells. Sequencing of diffuse large B-cell lymphoma (DLBCL) tumors and cell lines identified specific mutations in the forkhead domain linked to loss of function. Differential scanning calorimetry and thermal shift assays were used to characterize how eight of these mutations affect the stability of the FOX domain. Mutations L183P and L183R were found to be particularly destabilizing. Electrophoresis mobility shift assays show these same mutations also disrupt FOXO1 binding to their canonical DNA sequences, suggesting that the loss of function is due to destabilization of the folded structure. Computational modeling of the effect of mutations on FOXO1 folding was performed using alchemical free energy perturbation (FEP), and a Markov model of the entire folding reaction was constructed from massively parallel molecular simulations, which predicts folding pathways involving the late folding of helix α3. Although FEP can qualitatively predict the destabilization from L183 mutations, we find that a simple hydrophobic transfer model, combined with estimates of unfolded-state solvent-accessible surface areas from molecular simulations, is able to more accurately predict changes in folding free energies due to mutations. These results suggest that the atomic detail provided by simulations is important for the accurate prediction of mutational effects on folding stability. Corresponding disease-associated mutations in other FOX family members support further experimental and computational studies of the folding mechanism of FOX domains.

Oncogenic Mutations in the DNA-Binding Domain of FOXO1 that Disrupt Folding: Quantitative Insights from Experiments and Molecular Simulations.

FOXO1, a member of the family of winged-helix motif Forkhead box (FOX) transcription factors, is the most abundantly expressed FOXO member in mature B cells. Sequencing of diffuse large B-cell lymphoma (DLBCL) tumors and cell lines identified specific mutations in the forkhead domain linked to loss of function. Differential scanning calorimetry and thermal shift assays were used to characterize how eight of these mutations affect the stability of the FOX domain. Mutations L183P and L183R were found to be particularly destabilizing. Electrophoresis mobility shift assays show these same mutations also disrupt FOXO1 binding to their canonical DNA sequences, suggesting that the loss of function is due to destabilization of the folded structure. Computational modeling of the effect of mutations on FOXO1 folding was performed using alchemical free energy perturbation (FEP), and a Markov model of the entire folding reaction was constructed from massively parallel molecular simulations, which predicts folding pathways involving the late folding of helix α3. Although FEP can qualitatively predict the destabilization from L183 mutations, we find that a simple hydrophobic transfer model, combined with estimates of unfolded-state solvent-accessible surface areas from molecular simulations, is able to more accurately predict changes in folding free energies due to mutations. These results suggest that the atomic detail provided by simulations is important for the accurate prediction of mutational effects on folding stability. Corresponding disease-associated mutations in other FOX family members support further experimental and computational studies of the folding mechanism of FOX domains.

Interpretable pairwise distillations for generative protein sequence models.

Many different types of generative models for protein sequences have been proposed in literature. Their uses include the prediction of mutational effects, protein design and the prediction of structural properties. Neural network (NN) architectures have shown great performances, commonly attributed to the capacity to extract non-trivial higher-order interactions from the data. In this work, we analyze two different NN models and assess how close they are to simple pairwise distributions, which have been used in the past for similar problems. We present an approach for extracting pairwise models from more complex ones using an energy-based modeling framework. We show that for the tested models the extracted pairwise models can replicate the energies of the original models and are also close in performance in tasks like mutational effect prediction. In addition, we show that even simpler, factorized models often come close in performance to the original models.

DbMCS: A Database for Exploring the Mutation Markers of Anti-Cancer Drug Sensitivity.

The identification of mutation markers and the selection of appropriate treatment for patients with specific genome mutations are important steps in the development of targeted therapies and the realization of precision medicine for human cancers. To investigate the baseline characteristics of drug sensitivity markers and develop computational methods of mutation effect prediction, we presented a manually curated online-based database of mutation Markers for anti-Cancer drug Sensitivity (dbMCS). Currently, dbMCS contains 1271 mutations and 4427 mutation-disease-drug associations (3151 and 1276 for sensitivity and resistance, respectively) with their PubMed indexed articles. By comparing the mutations in dbMCS with the putative neutral polymorphisms, we investigated the characteristics of drug sensitivity markers. We found that the mutation markers tend to significantly impact on high-conservative regions both in DNA sequences and protein domains. And some of them presented pleiotropic effects depending on the tumor context, appearing concurrently in the sensitivity and resistance categories. In addition, we preliminarily explored the machine learning-based methods for identifying mutation markers of anti-cancer drug sensitivity and produced optimistic results, which suggests that a reliable dataset may provide new insights and essential clues for future cancer pharmacogenomics studies. dbMCS is available at http://bioinfo.aielab.cc/dbMCS/.

Sequence Neighborhoods Enable Reliable Prediction of Pathogenic Mutations in Cancer Genomes.

Simple SummaryCancer is caused by the accumulation of somatic mutations, some of which are responsible for the disease’s progression (drivers) while others are functionally neutral (passengers). Although several methods have been developed to distinguish between the two classes of mutations, very few have concentrated on using the neighborhood nucleotide sequences as potential discrimination features. In this study, we show that driver mutations’ neighborhood is significantly different from that of passengers. We further develop a novel machine learning tool, NBDriver, which is highly efficient at identifying pathogenic variants from multiple independent test datasets. Efficient and accurate identification of novel pathogenic variants from sequenced cancer genomes would help facilitate more effective therapies tailored to patients’ mutational profiles.Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. “Driver” mutations are primarily responsible for cancer progression, while “passengers” are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on using the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5′ and 3′ from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments provided comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural, and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with three other commonly used driver prediction tools (FATHMM (cancer), CONDEL, and MutationTaster) significantly outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of using raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.

MILAMP: Multiple Instance Prediction of Amyloid Proteins

Amyloid proteins are implicated in several diseases such as Parkinson's, Alzheimer's, prion diseases, etc. In order to characterize the amyloidogenicity of a given protein, it is important to locate the amyloid forming hotspot regions within the protein as well as to analyze the effects of mutations on these proteins. The biochemical and biological assays used for this purpose can be facilitated by computational means. This paper presents a machine learning method that can predict hotspot amyloidogenic regions within proteins and characterize changes in their amyloidogenicity due to point mutations. The proposed method called MILAMP (Multiple Instance Learning of AMyloid Proteins) achieves high accuracy for identification of amyloid proteins, hotspot localization, and prediction of mutation effects on amyloidogenicity by integrating heterogenous data sources and exploiting common predictive patterns across these tasks through multiple instance learning. The paper presents comprehensive benchmarking experiments to test the predictive performance of MILAMP in comparison to previously published state of the art techniques for amyloid prediction. The python code for the implementation and webserver for MILAMP is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#MILAMP.

ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of Mutations

The ELASPIC web server allows users to evaluate the effect of mutations on protein folding and protein-protein interaction on a proteome-wide scale. It uses homology models of proteins and protein-protein interactions, which have been precalculated for several proteomes, and machine learning models, which integrate structural information with sequence conservation scores, in order to make its predictions. Since the original publication of the ELASPIC web server, several advances have motivated a revisiting of the problem of mutation effect prediction. First, progress in neural network architectures and self-supervised pre-trained has resulted in models which provide more informative embeddings of protein sequence and structure than those used by the original version of ELASPIC. Second, the amount of training data has increased several-fold, largely driven by advances in deep mutation scanning and other multiplexed assays of variant effect. Here, we describe two machine learning models which leverage the recent advances in order to achieve superior accuracy in predicting the effect of mutation on protein folding and protein-protein interaction. The models incorporate features generated using pre-trained transformer- and graph convolution-based neural networks, and are trained to optimize a ranking objective function, which permits the use of heterogeneous training data. The outputs from the new models have been incorporated into the ELASPIC web server, available at http://elaspic.kimlab.org.

Unsupervised Inference of Protein Fitness Landscape from Deep Mutational Scan

The recent technological advances underlying the screening of large combinatorial libraries in high-throughput mutational scans deepen our understanding of adaptive protein evolution and boost its applications in protein design. Nevertheless, the large number of possible genotypes requires suitable computational methods for data analysis, the prediction of mutational effects, and the generation of optimized sequences. We describe a computational method that, trained on sequencing samples from multiple rounds of a screening experiment, provides a model of the genotype–fitness relationship. We tested the method on five large-scale mutational scans, yielding accurate predictions of the mutational effects on fitness. The inferred fitness landscape is robust to experimental and sampling noise and exhibits high generalization power in terms of broader sequence space exploration and higher fitness variant predictions. We investigate the role of epistasis and show that the inferred model provides structural information about the 3D contacts in the molecular fold.

Computational Prediction of Mutational Effects on SARS-CoV-2 Binding by Relative Free Energy Calculations.

The ability of coronaviruses to infect humans is invariably associated with their binding strengths to human receptor proteins. Both SARS-CoV-2, initially named 2019-nCoV, and SARS-CoV were reported to utilize angiotensin-converting enzyme 2 (ACE2) as an entry receptor in human cells. To better understand the interplay between SARS-CoV-2 and ACE2, we performed computational alanine scanning mutagenesis on the “hotspot” residues at protein–protein interfaces using relative free energy calculations. Our data suggest that the mutations in SARS-CoV-2 lead to a greater binding affinity relative to SARS-CoV. In addition, our free energy calculations provide insight into the infectious ability of viruses on a physical basis and also provide useful information for the design of antiviral drugs.

Prediction of mutation effects using a deep temporal convolutional network.

Accurate prediction of the effects of genetic variation is a major goal in biological research. Towards this goal, numerous machine learning models have been developed to learn information from evolutionary sequence data. The most effective method so far is a deep generative model based on the variational autoencoder (VAE) that models the distributions using a latent variable. In this study, we propose a deep autoregressive generative model named mutationTCN, which employs dilated causal convolutions and attention mechanism for the modeling of inter-residue correlations in a biological sequence. We show that this model is competitive with the VAE model when tested against a set of 42 high-throughput mutation scan experiments, with the mean improvement in Spearman rank correlation ∼0.023. In particular, our model can more efficiently capture information from multiple sequence alignments with lower effective number of sequences, such as in viral sequence families, compared with the latent variable model. Also, we extend this architecture to a semi-supervised learning framework, which shows high prediction accuracy. We show that our model enables a direct optimization of the data likelihood and allows for a simple and stable training process. Source code is available at https://github.com/ha01994/mutationTCN. Supplementary data are available at Bioinformatics online.

ISEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations.

Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning‐based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution, and Energy‐based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41 kcal/mol on a diverse training dataset consisting of 1102 mutations in 57 protein‐protein complexes. It competes with existing state‐of‐the‐art methods on two blind test datasets. Predictions for a new dataset of 487 mutations in 56 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC < 0.42), although their combination does improve the predictions. Feature analysis for iSEE underlines the significance of evolutionary conservations for quantitative prediction of mutation effects. As an application example, we perform a full mutation scanning of the interface residues in the MDM2–p53 complex.

Consistent Prediction of Mutation Effect on Drug Binding in HIV-1 Protease Using Alchemical Calculations.

Despite a large number of antiretroviral drugs targeting HIV-1 protease for inhibition, mutations in this protein during the course of patient treatment can render them inefficient. This emerging resistance inspired numerous computational studies of the HIV-1 protease aimed at predicting the effect of mutations on drug binding in terms of free binding energy Δ G, as well as in mechanistic terms. In this study, we analyze ten different protease-inhibitor complexes carrying major resistance-associated mutations (RAMs) G48V, I50V, and L90M using molecular dynamics simulations. We demonstrate that alchemical free energy calculations can consistently predict the effect of mutations on drug binding. By explicitly probing different protonation states of the catalytic aspartic dyad, we reveal the importance of the correct choice of protonation state for the accuracy of the result. We also provide insight into how different mutations affect drug binding in their specific ways, with the unifying theme of how all of them affect the crucial drug binding regions of the protease.

Population-specific design of de-immunized protein biotherapeutics.

Immunogenicity is a major problem during the development of biotherapeutics since it can lead to rapid clearance of the drug and adverse reactions. The challenge for biotherapeutic design is therefore to identify mutants of the protein sequence that minimize immunogenicity in a target population whilst retaining pharmaceutical activity and protein function. Current approaches are moderately successful in designing sequences with reduced immunogenicity, but do not account for the varying frequencies of different human leucocyte antigen alleles in a specific population and in addition, since many designs are non-functional, require costly experimental post-screening. Here, we report a new method for de-immunization design using multi-objective combinatorial optimization. The method simultaneously optimizes the likelihood of a functional protein sequence at the same time as minimizing its immunogenicity tailored to a target population. We bypass the need for three-dimensional protein structure or molecular simulations to identify functional designs by automatically generating sequences using probabilistic models that have been used previously for mutation effect prediction and structure prediction. As proof-of-principle we designed sequences of the C2 domain of Factor VIII and tested them experimentally, resulting in a good correlation with the predicted immunogenicity of our model.

Novel sequence variants in the MKKS gene cause Bardet-Biedl syndrome with intra- and inter-familial variable phenotypes.

Bardet-Biedl syndrome (BBS) is a multisystem disorder involving retina, kidney, limbs, and nervous system. Additional features associated with BBS include dental anomalies, cardiovascular defects, hearing loss, and speech impairment (Beales et al. 1999; Ullah et al. 2017). Diagnosis of BBS is based on the presence of four out of six primary or three primary and two secondary phenotypes. Variation in the phenotypes within and among different families has been reported (Beales et al. 1999; Khan et al. 2016). So far, mutations have been reported in 21 disease-causing genes (BBS1–21) (Heon et al. 2016; Suspitsin and Imyanitov, 2016). BBS segregates in autosomal recessive manner. In rare cases, triallelic nature of BBS involving three mutated alleles in two genes has been reported as well. Genes causing BBS phenotypes play important roles in ciliogenesis. Defects in the formation or function of cilia result in syndromic form of BBS. Cilia are cell surface structures involved in cell locomotion, signaling, and chemo/mechano/photo-sensation. Due to localization of cilia in different organs including kidney, liver, pancreas, odontoblasts, retinal photoreceptors, and hypothalamic neurons, ciliary defects cause multisystem disorders. Three BBS proteins including BB6/MKKS, BBS10, and BBS12 form chaperonin complex required for BBsome formation. BBsome is composed of eight BBS proteins including BBS1, BBS2, BBS4, BBS5, BBS7, BBS8, BBS9, and BBIP10/BBS18. Chaperonin binds to BBS7 and stabilizes it and then interacts with BBS2 and BBS9 to form core of BBsome. Other members bind to BBsome core in order of BBS1, BBS5, BBS8, BBS4, and BBIP10 (Loktev et al. 2008). BBsome participates in the cilia formation. The present study was conducted with the permission of institutional review board of Qauid-i-Azam University Islamabad, Pakistan. Clinical features of affected individuals are presented in Figure 1. Affected members in the two families (A and B) showed previously reported primary features of BBS, which included polydactyly, learning difficulties, retinitis pigmentosa, truncal obesity, and hypogonadism (Khan et al. 2016). In addition, a male affected member of family A showed sparse hairs on scalp and face, and a rare secondary feature of dental anomalies. In the same family, one of the affected members (V-5) had polydactyly in both hands and feet, while the second affected member (V-6) showed this feature only in the right foot. Affected members of family B showed clinodactyly of toes as a secondary feature of BBS (Table S1, Supporting information). DNA from blood was extracted using Sigma GenElute Blood Genomic DNA kit (Sigma Aldrich, St Louis, MO, USA). Homozygosity mapping was performed by genotyping highly polymorphic microsatellite markers which were mapped previously in vicinity of the BBS genes (BBS1–21) ( Fig. S1). This was followed by Sanger sequencing using standard procedure (Ullah et al. 2015), which revealed two novel variants including c.775-775delA in family A and c.287C>T in family B in the MKKS. Mutation effect prediction tools (MutationTaster, SIFT, and Polyphen2) predicted the two novel variants to be deleterious and disease-causing (Table S2). Previously, the MKKS was mapped on chromosome 20p12.2. The gene encodes 570-amino acid long BBS6 protein. It contains three domains including equatorial, intermediate, and apical. The equatorial domain is connected to apical domain by intermediate domain through flexible hinges. The variant (p.Thr259Leufs*21) in MKKS/BBS6, found in family A, is located in the apical domain which is involved in substrate binding. The other variant (p.Ala96Val), found in family B, is located in the most conserved equatorial domain and is highly likely to disrupt its ATPase activity. The BBS6 is a member of chaperonin family, which is involved in the formation of BBsome complex. It is highly likely that sequence variants in the BBS6 would disrupt formation of such a complex leading to buckle cilia formation. We have observed variation in the phenotypes not only in the two families, but also among patients of the same family (Table S1). Position and nature of mutation alter the structure and function of protein differentially. Genetic background and extent of change in the structure and function of protein might be responsible for the intra- and inter-familial variability of phenotypes. In conclusion, we have identified two novel sequence variants in the BBS6 causing BBS phenotypes in two different consanguineous families. This study not only supports the previously reported findings of intra- and inter-familial variations in features of BBS families, but also further extend spectrum of the mutations in the MKKS gene. This study will support genetic testing of BBS patients in Pakistan. We are highly thankful to the family members for their participation in the present research project. MU was supported by Higher Education Commission Islamabad, Pakistan Indigenous PhD fellowship. The authors declare that they have no competing interests. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Convert your favorite protein modeling program into a mutation predictor: "MODICT".

BackgroundPredict whether a mutation is deleterious based on the custom 3D model of a protein.ResultsWe have developed modict, a mutation prediction tool which is based on per residue rmsd (root mean square deviation) values of superimposed 3D protein models. Our mathematical algorithm was tested for 42 described mutations in multiple genes including renin (REN), beta-tubulin (TUBB2B), biotinidase (BTD), sphingomyelin phosphodiesterase-1 (SMPD1), phenylalanine hydroxylase (PAH) and medium chain Acyl-Coa dehydrogenase (ACADM). Moreover, modict scores corresponded to experimentally verified residual enzyme activities in mutated biotinidase, phenylalanine hydroxylase and medium chain Acyl-CoA dehydrogenase. Several commercially available prediction algorithms were tested and results were compared. The modict perl package and the manual can be downloaded from https://github.com/IbrahimTanyalcin/MODICT.ConclusionsWe show here that modict is capable tool for mutation effect prediction at the protein level, using superimposed 3D protein models instead of sequence based algorithms used by polyphen and sift.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1286-0) contains supplementary material, which is available to authorized users.

ISOexpresso: a web-based platform for isoform-level expression analysis in human cancer.

BackgroundAlternative splicing events that result in the production of multiple gene isoforms reveals important molecular mechanisms. Gene isoforms are often differentially expressed across organs and tissues, developmental stages, and disease conditions. Specifically, recent studies show that aberrant regulation of alternative splicing frequently occurs in cancer to affect tumor cell transformation and growth. While analysis of isoform expression is important for discovering tumor-specific isoform signatures and interpreting relevant genomic mutations, there is currently no web-based, easy-to-use, and publicly available platform for this purpose.DescriptionWe developed ISOexpresso to provide information regarding isoform existence and expression, which can be grouped by cancer vs. normal conditions, cancer types, and tissue types. ISOexpresso implements two main functions: First, the Isoform Expression View function creates visualizations for condition-specific RNA/isoform expression patterns upon query of a gene of interest. With this function, users can easily determine the major isoform (the most expressed isoform in a sample) of a gene with respect to the condition and check whether it matches the known canonical isoform. ISOexpresso outputs expression levels of all known transcripts to check alterations of expression landscape and to find potential tumor-specific isoforms. Second, the User Data Annotation function supports annotation of genomic variants to determine the most plausible consequence of a variation (e.g., an amino acid change) among many possible interpretations. As most coding sequence mutations are effective through the subsequent transcription and translation, ISOexpresso automatically prioritizes transcripts that act as backbones for mutation effect prediction by their relative expression. By employing ISOexpresso, we could investigate the consistency between the most expressed and known canonical/principal isoforms, as well as infer candidate tumor-specific isoforms based on their expression levels. In addition, we confirmed that ISOexpresso could easily reproduce previously known isoform expression patterns: recurrent observation of a major isoform across tissues, differential isoform expression patterns in a given tissue, and switching of major isoform during tumorigenesis.ConclusionsISOexpresso serves as a web-based, easy-to-use platform for isoform expression and alteration analysis based on large-scale cancer database. We anticipate that ISOexpresso will expedite formulation and confirmation of novel hypotheses by providing isoform-level perspectives on cancer research. The ISOexpresso database is available online at http://wiki.tgilab.org/ISOexpresso/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2852-6) contains supplementary material, which is available to authorized users.