Mechismo

Elevator pitch

Mechismo is a tool that lets you find potential mechanisms for how proteins interact with other molecules and more importantly for how any changes that might affect these interactions, and consequently an entire biological system. We have constructed a database of roughly 50,000 protein-protein, protein-small-molecule and protein-nucelic acid interactions of known structure and several million interactions identified by other methods, all of which we use to identify or predict potential interactions within a set of proteins or genes provided by you.

Publications using mechismo

The primary reference for mechismo is:
Betts MJ, Lu Q, Jiang YY, Drusko A, Wichmann A, Utz M, Valtierra-GutiÃ©rrez IA, Schlesner M, Jaeger N, Jones DT, Pfister S, Lichter P, Eils R, Siebert R, Bork B, Apic G, Gavin AC, Russell RB
Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions
Nucl Acids Res, 43(2):e10, 2015 (PubMed).

Mechismo, or the data behind it, has been used in a number of studies:
Our group:

Raimondi et al, Rare, functional, somatic variants in gene families linked to cancer genes: GPCR signaling as a paradigm. Oncogene 2019 PubMed
Groeschel et al, Defective homologous recombination DNA repair as therapeutic targt in advanced chordoma. Nat Commun 2019 PubMed
Lopez et al, Genomic and transcriptomic changes complement each other in the pathogenesis of sporadic Burkitt lymphoma. Nat Commun 2019 PubMed
Wagener et al, The mutational landscape of Burkitt-like lymphoma with 11q aberration is distinct from that of Burkitt lymphoma. Blood 2019 PubMed
Kishore et al, Understanding the role of genetic variability in LRRK2 in Indian population. Mov Disord 2019 PubMed
Vitale et al, Cryptogenic cholestasis in young and adults: ATP8B1, ABCB11, ABCB4, and TJP2 gene variants analysis by high-throughput sequencing. J. Gastroenterol 2017 PubMed
Raimondi et al, Natural genetic variants affecting equivalent protein family positions reflect human diversity. Sci. Rep. 2017 PubMed
Betts et al, Systematic identification of phosphorylation-mediated protein interaction switches. PLoS Comp. Biol. 2017 PubMed
Raimondi, Singh et al, Insights into cancer severity from biomolecular interaction mechanisms. Sci. Rep. 2016 PubMed
Boldt et al, An organelle-specific protein landscape identifies novel diseases and molecular mechanisms. Nat Commun. 2016 PubMed
Lopez et al. Genes encoding members of the JAK-STAT pathway or epigenetic regulators are recurrently mutated in T-cell prolymphocytic leukaemia. Br J Haematol. 2016 PubMed
Kretzmer et al. DNA methylome analysis in Burkitt and follicular lymphomas identifies differentially methylated regions linked to somatic mutation and transcriptional control. Nat Genet. 2015 PubMed
Wagener et al. The PCBP1 gene encoding poly(rc) binding protein I is recurrently mutated in Burkitt lymphoma. Genes Chromosomes Cancer. 2015 PubMed
Vater et al. The mutational pattern of primary lymphoma of the central nervous system determined by whole exomoe sequencing. Leukemia. 2014 PubMed.
Hasselblatt et al. SMARCA4-mutated atypical teratoid/rhabdoid tumors are associated with inherited germline alterations and poor prognosis. Acta Neuropathol. 2014 PubMed.
Rohde et al. Recurrent RHOA mutations in pediatric Burkitt lymphoma treated according to the NHL-BFM protocols. Genes Chromosomes Cancer. 2014 PubMed.
Bergmann et al. Recurrent mutation of JAK3 in T-cell prolymphocytic leukemia. Genes Chromosomes Cancer. 2014 PubMed.
Salaverria et al. A recurrent 11q aberration pattern characterizes a subset of MYC-negative high-grade B-cell lymphomas resembling Burkitt lymphoma. Blood. 2014 PubMed.
Richter et al. Recurrent mutation of the ID3 gene in Burkitt lymphoma identified by integrated genome, exome and transcriptome sequencing. Nat Genet. 2012 PubMed.
Jones et al. Dissecting the genomic complexity underlying medulloblastoma. Nature. 2012 PubMed.
van Noort et al. Cross-talk between phosphorylation and lysine acetylation in a genome-reduced bacterium. Mol Syst Biol. 2012 PubMed.

Other groups:

Fancello et al. The ribosomal protein gene RPL5 is a haploinsufficient tumor suppressor in multiple cancer types. Oncotarget 2017. PubMed.
Porta-Pardo & Godzik. Mutation Drivers of Immunological Responses to Cancer. Cancer Immunol. Res. 2016 PubMed.
Sahni et al. Widespread macromolecular interaction perturbations in human genetic disorders. Cell. 2015 PubMed.
Yang et al. Protein domain-level landscape of cancer-type-specific somatic mutations. PLoS Comput Biol. 2015 PubMed.

More about Mechismo

The primary data behind Mechismo consists of:

Three-dimensional structures from the Protein Databank or PDB
Sequences and annotation from Uniprot
Protein-protein Interaction from PSICQUIC (a collation of most interaction databases)
Domain annotations from Pfam

The current data dates from Jan 2014 (PDB and interactions).

For our current list of supported organisms/proteomes (Human, Mouse, C.elegans, D.melanogaster, S.cerivisae, E.coli, B.subtilis and M.pneumoniae) we have compared all sequences in Uniprot SPROT/VARSPLIC to the sequences of known structure using BLAST/SIFTS to obtain a list of significant matches between the model organisms and sequences of known structure. We use these matches to identify known (i.e. identical or near identical) or predicted (i.e. lower sequence similarity) sites in contact with proteins, chemicals or nucleic acids.

Each site in known or predicted contact with another molecules is assigned a confidence that we have derived by a careful analysis of the entire dataset based on the expected False Positive Rate (FRP) as a function of percentage sequence identity between the sequence of interest and the particular template of known structure. As sequence similarity decreases, so does the confidence in the prediction.

For protein-protein interactions, we collated all interactions and grouped them carefully according to Uniref classifications (into Uniref100, Uniref90, Uniref50). This allows (e.g.) interactions within Mouse to be used as evidence of an interaction between Human proteins, though how the interactions are used depends on your stringency settings. Via the standard or advanced interface you can specify only interactions of great confidence (e.g. if studying a cancer genome) or allow for weaker evidence (e.g. if exploring Yeast or Bacterial interactions) as suits your particular case.

Additionally for protein-protein interactions, we have established a metric for assessing whether any provided mutation or modification will increase or decrease the affinity at the interface. To do this we used an updated matrix of amino-acid interaction preferences that says how favorable/disfavorable a pair of interacting amino acids is. For example, if a hydrophobic residue (e.g. Leucine) in a hydrophobic pocket is mutated to a charged residues (e.g. Aspartate) then this matrix will suggest a negative (i.e. deterimental) score for the change.

For chemicals we have performed an automated classification via chemoinformatics tools, which we then manually processed to assign chemicals into about 50 groups. These groups provide specific information for chemicals or chemical types that are abundant in known structures (e.g. ATP-like, Zn++), and general properties (e.g. Organic) for chemicals that are unique.

Predicting effect of modifications on interactions

For changes at interfaces we use empirical pair potentials which score the likelihood of amino acid side chains interacting across an interface. Essentially these are log odds values with positive values indicating favored pairings and negative values unfavorable pairings. When considering a change of an amino acid, we sum the scores for the original amino acid and subtract these from the sum for the changed amino acid. Thus positive scores indicate a change that is expected to improve the interface (i.e. to increase affinity), and negative scores indicate a change expected to worsen it.

The matrix of these values for protein-protein interactions can be seen here. These are an updated version of those first described in the paper describing the InterPReTS method. The values for protein-chemical and protein-DNA/RNA interactions can be seen here.

Confidence in predictions

It has long been known that the lower the sequence identity between a sequence and the template on which it is modelled, the less likely the predictions are to be correct. To put specific numbers on this we performed an assessment of how accurate predictions are by studying known structures. Specifically, we cacluated false positive rates for predicting known sites using templates at various levels of identity. Below is the table used to define the confidence intervals shown in the software.

FPR     conf.  Prot-chem Prot-DNA/RNA Prot-prot
 <0.500 low       <34        <41          <29
>=0.500 medium   >=34       >=41         >=29
>=0.200 medium   >=47       >=51         >=34
>=0.100 high     >=64       >=56         >=37
>=0.050 high     >=81       >=86         >=40
>=0.010 high     >=90       >=90         >=60

For instance if one predicts a DNA binding site with an template sharing 58% identity to a Yeast protein, then the expected false-positive rate is approximately 10% (which we define as high confidence). The low/medium/high confidence limits were deduced by a consideration of the balance of positives and negatives in the dataset overall and the overall accuracy ( (TP + TN)/(TP + FP + TN + FN)), which is low <53%, medium (53-78%) and high >=78% (average values give across the three types).

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