From 59e7c15f1fc5322e273b612779229d5e84f5e11c Mon Sep 17 00:00:00 2001 From: Eduard Kerkhoven Date: Fri, 5 Oct 2018 09:11:51 +0200 Subject: [PATCH 01/14] refactor: manualModifications speed-up - no need to make full S-matrix in every iteration --- geckomat/change_model/manualModifications.m | 944 ++++++++++---------- 1 file changed, 472 insertions(+), 472 deletions(-) diff --git a/geckomat/change_model/manualModifications.m b/geckomat/change_model/manualModifications.m index 15ba27b75..cb707df7b 100644 --- a/geckomat/change_model/manualModifications.m +++ b/geckomat/change_model/manualModifications.m @@ -1,472 +1,472 @@ -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% model = manualModifications(model) -% -% Benjamin J. Sanchez. Last edited: 2017-10-29 -% Ivan Domenzain. Last edited: 2018-05-28 -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - -function [model,modifications] = manualModifications(model) - -%Read manual data: -fID = fopen('../../databases/manual_data.txt'); -data = textscan(fID,'%s %s %s %s %f','delimiter','\t'); -structure = data{2}; -protGenes = data{4}; -kcats = data{5}.*3600; -data = load('../../databases/ProtDatabase.mat'); -swissprot = data.swissprot; -kegg = data.kegg; -fclose(fID); -modifications{1} = cell(0,1); -modifications{2} = cell(0,1); - -%Construct curated complexes: -uniprots = cell(size(kcats)); -stoich = cell(size(kcats)); -for i = 1:length(kcats) - uniprots{i} = strsplit(structure{i},' + '); - stoich{i} = ones(size(uniprots{i})); - %Separate complex strings in units and amount of each unit: - for j = 1:length(uniprots{i}) - unit = uniprots{i}{j}; - pos = strfind(unit,' '); - if isempty(pos) - stoich{i}(j) = 1; - uniprots{i}{j} = unit; - else - stoich{i}(j) = str2double(unit(1:pos-1)); - uniprots{i}{j} = unit(pos+1:end); - end - end -end - -for i = 1:length(model.rxns) - reaction = model.rxnNames{i}; - %Find set of proteins present in rxn: - S = full(model.S); - subs_pos = find(S(:,i) < 0); - prot_pos = find(~cellfun(@isempty,strfind(model.mets,'prot_'))); - int_pos = intersect(subs_pos,prot_pos); - prot_set = cell(size(int_pos)); - MW_set = 0; - for j = 1:length(int_pos) - met_name = model.mets{int_pos(j)}; - prot_set{j} = met_name(6:end); - MW_set = MW_set + model.MWs(strcmp(model.enzymes,prot_set{j})); - end - %Find intersection with manual curated data: - for j = 1:length(uniprots) - int = intersect(prot_set,uniprots{j}); - if length(int)/max(length(prot_set),length(uniprots{j})) > 0.50 % 50% match - %Erase previous protein stoich. coeffs from rxn: - for k = 1:length(prot_set) - model.S(int_pos(k),i) = 0; - end - %If some proteins where not present previously, add them: - newMets = uniprots{j}; - grRule = protGenes{j}; - for k = 1:length(uniprots{j}) - if sum(strcmp(model.enzymes,uniprots{j}{k})) == 0 - model = addProtein(model,uniprots{j}{k},kegg,swissprot); - end - newMets{k} = ['prot_' newMets{k}]; - end - %Add new protein stoich. coeffs to rxn: - kvalues = kcats(j)./stoich{j}; - rxnID = model.rxns{i}; - rxnName = model.rxnNames{i}; - model = addEnzymesToRxn(model,kvalues,rxnID,newMets,{rxnID,rxnName},grRule); - end - end - %Update int_pos: - S = full(model.S); - subs_pos = find(S(:,i) < 0); - %Get the proteins that are part of the i-th rxn - prot_pos = find(~cellfun(@isempty,strfind(model.mets,'prot_'))); - int_pos = intersect(subs_pos,prot_pos)'; - %%%%%%%%%%%%%%%%%%%%%%%%%%% Individual Changes: %%%%%%%%%%%%%%%%%%%%%%%% - for j = 1:length(int_pos) - enzName = model.mets(int_pos(j)); - %%%%%%%%%%%%%%%%%% MANUAL CURATION FOR TOP GROWTH LIMITING ENZYMES: - [newValue,modifications] = curation_growthLimiting(reaction,enzName,MW_set,modifications); - if ~isempty(newValue) - model.S(int_pos(j),i) = newValue; - else - %%%%%%%%%%%%%%%%%%%% MANUAL CURATION FOR CARBON SOURCES - [newValue,modifications] = curation_carbonSources(reaction,enzName,MW_set,modifications); - if ~isempty(newValue) - model.S(int_pos(j),i) = newValue; - else - %%%%%%%%%%%%%%% MANUAL CURATION FOR TOP USED ENZYMES: - [newValue,modifications] = curation_topUsedEnz(reaction,enzName,MW_set,modifications); - if ~isempty(newValue) - model.S(int_pos(j),i) = newValue; - end - end - end - end - disp(['Improving model with curated data: Ready with rxn #' num2str(i)]) -end - -%%%%%%%%%%%%%%%%%%%%%%%%% Other manual changes: %%%%%%%%%%%%%%%%%%%%%%%%%%% -model = otherChanges(model); -% Remove repeated reactions (2017-01-16): -rem_rxn = false(size(model.rxns)); -for i = 1:length(model.rxns)-1 - for j = i+1:length(model.rxns) - if isequal(model.S(:,i),model.S(:,j)) && model.lb(i) == model.lb(j) && ... - model.ub(i) == model.ub(j) - rem_rxn(j) = true; - disp(['Removing repeated rxn: ' model.rxns{i} ' & ' model.rxns{j}]) - end - end -end -model = removeReactions(model,model.rxns(rem_rxn),true); -% Merge arm reactions to reactions with only one isozyme (2017-01-17): -arm_pos = zeros(size(model.rxns)); -p = 0; -for i = 1:length(model.rxns) - rxn_id = model.rxns{i}; - if contains(rxn_id,'arm_') - rxn_code = rxn_id(5:end); - k = 0; - for j = 1:length(model.rxns) - if ~isempty(strfind(model.rxns{j},[rxn_code 'No'])) - k = k + 1; - pos = j; - grRule = model.grRules{j}; - end - end - if k == 1 - %Condense both reactions in one: - model = addReaction(model,model.rxns{pos}, ... - 'reactionName', model.rxnNames{pos}, ... - 'metaboliteList', model.mets, ... - 'stoichCoeffList', model.S(:,i) + model.S(:,pos), ... - 'lowerBound', 0, ... - 'upperBound', 1000); - model.grRules{pos} = grRule; - p = p + 1; - arm_pos(p) = i; - disp(['Merging reactions: ' model.rxns{i} ' & ' model.rxns{pos}]) - end - end -end -% Remove saved arm reactions: -model = removeReactions(model,model.rxns(arm_pos(1:p)),true); - -% Remove unused enzymes after manual curation (2017-01-16): -rem_enz = false(size(model.enzymes)); -for i = 1:length(model.enzymes) - pos_met = strcmp(model.mets,['prot_' model.enzymes{i}]); - if sum(model.S(pos_met,:)~=0) == 1 - rem_enz(i) = true; - end -end -rem_enz = model.enzymes(rem_enz); -for i = 1:length(rem_enz) - model = deleteProtein(model,rem_enz{i}); - disp(['Removing unused protein: ' rem_enz{i}]) -end - -% Block O2 and glucose production (for avoiding multiple solutions): -model.ub(strcmp(model.rxnNames,'oxygen exchange')) = 0; -model.ub(strcmp(model.rxnNames,'D-glucose exchange')) = 0; - -% Remove incorrect pathways: -model = removeIncorrectPathways(model); - -% Map the index of the modified Kcat values to the new model (after rxns removals) -modifications = mapModifiedRxns(modifications,model); - -end - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% Modify the top growth limiting enzymes that were detected by the -% modifyKcats.m script in a preliminary run. -function [newValue,modifications] = curation_growthLimiting(reaction,enzName,MW_set,modifications) - newValue = []; - reaction = string(reaction); - % Ketol-acid Reductoisomerase (P06168/EC1.1.1.86) - 2-acetyllactic - % acid: Substrate name in BRENDA was a synonim as name in model. - % Changed manually (2015-08-28). - if strcmpi('prot_P06168',enzName) - if contains(reaction,'acetohydroxy acid isomeroreductase (') - newValue = -(18.3*3600)^-1; %BRENDA: 2-acetolactate - modifications{1} = [modifications{1}; 'P06168']; - modifications{2} = [modifications{2}; reaction]; - end - end - % HMG-CoA reductase (P12683-P12684/EC1.1.1.34): Only kcat available - % in BRENDA was for Rattus Norvegicus. Value corrected with a - % s.a. from the Haloferax volcanii enzyme expressed in E. coli (max - % value available in BRENDA for a microorganism, without counting - % recombinant proteins) (2018-08-11). Ref: Bischoff & Rodwell 1996 - % https://www.ncbi.nlm.nih.gov/pmc/articles/PMC177616/ - if (strcmpi('prot_P12683',enzName)||strcmpi('prot_P12684',enzName)) && ... - contains(reaction,'hydroxymethylglutaryl') - newValue = -(24*60*1e3/1e3*MW_set)^-1; - modifications{1} = [modifications{1}; 'P12683']; - modifications{2} = [modifications{2}; reaction]; - end - % [1.2.1.12] Kcat (Sce & natural substrate) 29 [1/s] is a highly - % growthRate limiting value, S.A. 100 (Sce) - %enzIDs = {'prot_P00359','prot_P00360','prot_P00358'}; - if contains(reaction,'glyceraldehyde-3-phosphate dehydrogenase') - if strcmpi('prot_P00360',enzName) - newValue = -(29*3600)^-1; - %newValue = -(100*1e3/1e3*MW_set)^-1; - modifications{1} = [modifications{1}; 'P00360']; - modifications{2} = [modifications{2}; reaction]; - elseif strcmpi('prot_P00358',enzName) - newValue = -(16.7*3600)^-1; - %newValue = -(100*1e3/1e3*MW_set)^-1; - modifications{1} = [modifications{1}; 'P00358']; - modifications{2} = [modifications{2}; reaction]; - elseif strcmpi('prot_P00359',enzName) - newValue = -(9.1*3600)^-1; - %newValue = -(100*1e3/1e3*MW_set)^-1; - modifications{1} = [modifications{1}; 'P00359']; - modifications{2} = [modifications{2}; reaction]; - end - end - % [4.1.1.-, 4.1.1.43, 4.1.1.72, 4.1.1.74] Pyruvate decarboxylase - % Resulted to be a growth limiting enzyme but the Kcat - % value seems to be the best candidate for this reaction (rev) - if strcmpi('prot_P06169',enzName) && contains(reaction,'pyruvate decarboxylase') - %newValue = -1/(73.1*3600); - newValue = -1/(145*3600); - modifications{1} = [modifications{1}; 'P06169']; - modifications{2} = [modifications{2}; reaction]; - end - % [Q00955//EC6.4.1.2] Acetyl-CoA carboxylase - % Assigned value was 1.23 (1/s), S.A. was used instead (6 - % umol/min/mg) from BRENDA (2018-01-22). - if strcmpi('prot_Q00955',enzName) && contains(reaction,'acetyl-CoA carboxylase') - newValue = -(6*60*1e3/1e3*MW_set)^-1; - modifications{1} = [modifications{1}; 'Q00955']; - modifications{2} = [modifications{2}; reaction]; - end - % [P52867//EC2.4.1.109] mannosyltransferase - % Assigned value was 0.0053 (1/s) for Sce, however this value yields - % a growth control coefficient 0f 0.9316. The highest kinetic - % parameter reported for this EC number is the one that was - % assigned, a wild card is introduced into the EC number and the highest - % value for a similar metabolite was chosen instead: - % 70.9 [1/s] reported for EC2.4.1.83 in Saccharomyces cerevisiae - % from BRENDA (2018-04-10). - if strcmpi('prot_P52867',enzName) && contains(reaction,'dolichyl-phosphate-mannose--protein mannosyltransferase') - newValue = -(70.9*3600)^-1; - modifications{1} = [modifications{1}; 'P52867']; - modifications{2} = [modifications{2}; reaction]; - end - % [P52867//EC2.5.1.6] methionine adenosyltransferase. Highly growth - % limiting enzyme. No reported value for S. cerevisiae, Kcat for - % Rattus norvegicus used instead <- 0.583 [1/s]. - % from BRENDA (2018-04-10). - if strcmpi('prot_P10659',enzName) && contains(reaction,'methionine adenosyltransferase') - newValue = -(0.583*3600)^-1; - modifications{1} = [modifications{1}; 'P10659']; - modifications{2} = [modifications{2}; reaction]; - end -end - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% Modify those kcats involved in extreme misspredictions for growth on -% several carbon sources. This values were obtained by specific searches on -% the involved pathways for the identification of the ec numbers and then -% its associated Kcat values were gotten from BRENDA. -function [newValue,modifications] = curation_carbonSources(reaction,enzName,MW_set,modifications) - newValue = []; - reaction = string(reaction); - % alpha,alpha-trehalase (P32356/EC3.2.1.28): The growth on - % alpha,alpha-trehalose was extremely highly overpredicted, the - % minimum substrate specific kcat value (0.67 1/s) is chosen instead - % (Metarhizium flavoviride) - % from BRENDA (2018-01-22). - if (strcmpi('prot_P32356',enzName) || strcmpi('prot_P48016',enzName)) && ... - contains(reaction,'alpha,alpha-trehalase') - newValue = -(0.67*60*1e3/1e3*MW_set)^-1; - modifications{1} = [modifications{1}; 'P48016']; - modifications{2} = [modifications{2}; reaction]; - end - % alcohol dehydrogenase (ethanol to acetaldehyde)[P00331/EC1.1.1.1] - % Growth on ethanol was extremely highly overpredicted, the - % specific Kcat value for ethanol found in BRENDA is 143 (1/s) @pH - % 8.0 and 25 C, and 432.3 @20?C and pH 9.0 - % from BRENDA (2018-01-22). - if strcmpi('prot_P00331',enzName) && contains(reaction,'alcohol dehydrogenase (ethanol to acetaldehyde)') - newValue = -(143*3600)^-1; - modifications{1} = [modifications{1}; 'P00331']; - modifications{2} = [modifications{2}; reaction]; - end - % Glycerol dehydrogenase [P14065/EC1.1.1.1] - % Growth on glycerol was highly overpredicted, the minimum S.A. - % value for glycerol found in BRENDA is 0.54 (umol/ming/mg) and - % the maximal is 15.7, both for Schizosaccharomyces pombe. The MW - % is 57 kDa. From BRENDA (2018-01-26). - if strcmpi('prot_P14065',enzName) && contains(reaction,'glycerol dehydrogenase (NADP-dependent)') - newValue = -(0.54*57000*0.06)^-1; - modifications{1} = [modifications{1}; 'P14065']; - modifications{2} = [modifications{2}; reaction]; - end - % alpha-glucosidase [EC3.2.1.10/EC3.2.1.20] - % Growth on maltose is still underpredicted, for this reason all - % the isoenzymes catalysing its conversion are set up to the - % maximal glucosidase rate for maltose (3.2.1.20) <- 709 1/s for - % Schizosaccharomyces pombe, a value of 1278 is also available for - % Sulfolobus acidocaldarius. - % From BRENDA (2018-01-28). - if contains(reaction,'alpha-glucosidase') - newValue = -(1278*3600)^-1; - end -end - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% After the growth limiting Kcats analysis and the curation for several -% carbon sources, a simulation for the model growing on minimal glucose -% media yielded a list of the top used enzymes (mass-wise), those that were -% taking more than 10% of the total proteome are chosen for manual curation -function [newValue,modifications] = curation_topUsedEnz(reaction,enzName,MW_set,modifications) - newValue = []; - reaction = string(reaction); - % amidophosphoribosyltransferase [P04046/EC2.4.2.14] - % No Kcat reported on BRENDA, the maximum S.A. (E. coli is taken - % instead) - if strcmpi('prot_P04046',enzName) && contains(reaction,'phosphoribosylpyrophosphate amidotransferase') - newValue = -(17.2*194000*60/1000)^-1; - modifications{1} = [modifications{1}; 'P04046']; - modifications{2} = [modifications{2}; reaction]; - end - % Glutamate N-acetyltransferase (Q04728/EC2.3.1.1): Missanotated - % EC (should be 2.3.1.35), and no kcats for the correct EC. were - % found. Value corrected with s.a. in S.cerevisiae - % from BRENDA (2015-08-31). - if strcmpi('prot_Q04728',enzName) && contains(reaction,'ornithine transacetylase (No1)') - newValue = -(22*60*1e3/1e3*MW_set)^-1; %22 [umol/min/mg] - modifications{1} = [modifications{1}; 'Q04728']; - modifications{2} = [modifications{2}; reaction]; - end - % atp synthase mitochondrial (P07251-P00830/EC3.6.3.14): No Kcat - % reported for S. cerevisiae, value for Bacillus sp. - % for ATP is used instead 217 [1/s] - % from BRENDA (2017-01-18). - if (strcmpi('prot_P07251',enzName) || strcmpi('prot_P00830',enzName))&& ... - contains(reaction,'ATP synthase (No1)') - newValue = -(390*3600)^-1; - modifications{1} = [modifications{1}; 'P07251']; - modifications{2} = [modifications{2}; reaction]; - end - % transaldolase (reversible) (P15019/EC2.2.1.2): - % The protein usage is represents around 10% of the used proteome - % on several carbon sources (batch simulations). The highest S.A. - % was used instead (60*75000*60/1000) [1/hr] for E. coli - % from BRENDA (2017-01-18). - if strcmpi('prot_P15019',enzName) && contains(reaction,'transaldolase (reversible)') - newValue = -(60*75000*60/1000)^-1; - modifications{1} = [modifications{1}; 'P15019']; - modifications{2} = [modifications{2}; reaction]; - end - % [Q06817//EC6.1.1.14] glycyl-tRNA synthetase - % Resulted to be a growth limiting enzyme, maximum catalytic value - % found by the automatic algorithm was 15.9 1/s - % from BRENDA (2018-01-22). - if strcmpi('prot_Q06817',enzName) && contains(reaction,'glycyl-tRNA synthetase') - newValue = -1/(15.9*3600); - modifications{1} = [modifications{1}; 'Q06817']; - modifications{2} = [modifications{2}; reaction]; - end - % P80235/EC 2.3.1.7 - carnitine O-acetyltransferase - % The assigned Kcat was for Mus musculus and resulted to take - % 20% of the used proteome on batch simulation for several - % carbon sources. Sce S.A. is used instead [200 umol/min/mg] - % from BRENDA (2018-01-18) - if strcmpi('prot_P80235',enzName) && contains(reaction,'carnitine O-acetyltransferase') - newValue = -(200*60*1e3/1e3*MW_set)^-1; %22 [umol/min/mg] - modifications{1} = [modifications{1}; 'P80235']; - modifications{2} = [modifications{2}; reaction]; - end - % FAS (P07149+P19097/EC2.3.1.86): No kcat available in BRENDA - % (it was using ECs of subunits: EC3.1.2.14 and EC2.3.1.41).Value - % corrected with max. s.a. in S.cerevisiae for NADPH in BRENDA - % (2015-08-25) - if (strcmpi('prot_P07149',enzName) || strcmpi('prot_P19097',enzName)) - if contains(reaction,'fatty-acyl-CoA synthase (n-C16:0CoA)') - newValue = -(3/14*60*1e3/1e3*MW_set)^-1; %3/14 [umol/min/mg] - elseif contains(reaction,'fatty-acyl-CoA synthase (n-C18:0CoA)') - newValue = -(3/16*60*1e3/1e3*MW_set)^-1; %3/16 [umol/min/mg] - end - modifications{1} = [modifications{1}; 'P07149']; - modifications{2} = [modifications{2}; reaction]; - modifications{1} = [modifications{1}; 'P19097']; - modifications{2} = [modifications{2}; reaction]; - end - % Enolase (1&2) [4.2.1.11] 71.4 (1/s) is the Kcat reported for - % 2-phospho-D-glycerate - % 230 (1/s) is the Kcat reported for 2-phosphoglycerate - % both measurements are for native enzymes - if strcmpi('prot_P00924',enzName) && contains(reaction,'enolase') - newValue = -1/(230*3600); - modifications{1} = [modifications{1}; 'P00924']; - modifications{2} = [modifications{2}; reaction]; - end -end - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - -function model = otherChanges(model) - % Remove protein P14540 (Fructose-bisphosphate aldolase) from missanotated rxns - % (2017-01-16): - rxns_tochange = {'D-fructose 1-phosphate D-glyceraldehyde-3-phosphate-lyase (No1)';... - 'sedoheptulose 1,7-bisphosphate D-glyceraldehyde-3-phosphate-lyase (No1)';... - 'D-fructose 1-phosphate D-glyceraldehyde-3-phosphate-lyase (reversible) (No1)';... - 'sedoheptulose 1,7-bisphosphate D-glyceraldehyde-3-phosphate-lyase (reversible) (No1)'}; - for i = 1:length(rxns_tochange) - rxn_name = rxns_tochange{i}; - pos_rxn = find(strcmpi(model.rxnNames,rxn_name)); - rxn_id = model.rxns{pos_rxn}; - model.S(strcmpi(model.mets,'prot_P14540'),pos_rxn) = 0; - model.rxns{pos_rxn} = rxn_id(1:end-3); - end - - % Remove protein P40009 (Golgi apyrase) from missanotated rxns (2016-12-14): - pos_rxn = find(~cellfun(@isempty,strfind(model.rxnNames,'ATPase, cytosolic (No3)'))); - if ~isempty(pos_rxn) && pos_rxn~=0 - model = removeReactions(model,model.rxns(pos_rxn)); - end - - % Remove 2 proteins from missanotated rxns: Q12122 from cytosolic rxn (it's - % only mitochondrial) & P48570 from mitochondrial rxn (it's only cytosolic). - % Also rename r_0543No2 to r_0543No1 (for consistency) (2017-08-28): - model = removeReactions(model,{'r_0543No1'}); - model = removeReactions(model,{'r_1838No2'}); - index = find(strcmp(model.rxns,'r_0543No2')); - if ~isempty(index) - model.rxnNames{index} = 'homocitrate synthase (No1)'; - model.rxns{index} = 'r_0543No1'; - end - % Aconitase (P19414/EC 4.2.1.3): The rxn is represented as a two - % step rxn in Yeast7.5, so the kcat must be multiplied by 2 - %(2015-11-05) - index = find(strcmpi('prot_P19414',model.mets)); - if ~isempty(index) - rxnIndxs = find(model.S(:,index)); - if ~isempty(rxnIndxs) - rxnIndxs = rxnIndxs(1:end-2); - model.S(rxnIndxs,index) = model.S(rxnIndxs,index)/2; - end - end -end - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - -function modified = mapModifiedRxns(modifications,model) - modified = []; - for i=1:length(modifications{1}) - rxnIndex = find(strcmp(model.rxnNames,modifications{2}(i)),1); - str = {horzcat(modifications{1}{i},'_',num2str(rxnIndex))}; - modified = [modified; str]; - end -end - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% model = manualModifications(model) +% +% Benjamin J. Sanchez. Last edited: 2017-10-29 +% Ivan Domenzain. Last edited: 2018-05-28 +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [model,modifications] = manualModifications(model) + +%Read manual data: +fID = fopen('../../databases/manual_data.txt'); +data = textscan(fID,'%s %s %s %s %f','delimiter','\t'); +structure = data{2}; +protGenes = data{4}; +kcats = data{5}.*3600; +data = load('../../databases/ProtDatabase.mat'); +swissprot = data.swissprot; +kegg = data.kegg; +fclose(fID); +modifications{1} = cell(0,1); +modifications{2} = cell(0,1); + +%Construct curated complexes: +uniprots = cell(size(kcats)); +stoich = cell(size(kcats)); +for i = 1:length(kcats) + uniprots{i} = strsplit(structure{i},' + '); + stoich{i} = ones(size(uniprots{i})); + %Separate complex strings in units and amount of each unit: + for j = 1:length(uniprots{i}) + unit = uniprots{i}{j}; + pos = strfind(unit,' '); + if isempty(pos) + stoich{i}(j) = 1; + uniprots{i}{j} = unit; + else + stoich{i}(j) = str2double(unit(1:pos-1)); + uniprots{i}{j} = unit(pos+1:end); + end + end +end + +for i = 1:length(model.rxns) + reaction = model.rxnNames{i}; + %Find set of proteins present in rxn: + S = model.S; + subs_pos = find(S(:,i) < 0); + prot_pos = find(~cellfun(@isempty,strfind(model.mets,'prot_'))); + int_pos = intersect(subs_pos,prot_pos); + prot_set = cell(size(int_pos)); + MW_set = 0; + for j = 1:length(int_pos) + met_name = model.mets{int_pos(j)}; + prot_set{j} = met_name(6:end); + MW_set = MW_set + model.MWs(strcmp(model.enzymes,prot_set{j})); + end + %Find intersection with manual curated data: + for j = 1:length(uniprots) + int = intersect(prot_set,uniprots{j}); + if length(int)/max(length(prot_set),length(uniprots{j})) > 0.50 % 50% match + %Erase previous protein stoich. coeffs from rxn: + for k = 1:length(prot_set) + model.S(int_pos(k),i) = 0; + end + %If some proteins where not present previously, add them: + newMets = uniprots{j}; + grRule = protGenes{j}; + for k = 1:length(uniprots{j}) + if sum(strcmp(model.enzymes,uniprots{j}{k})) == 0 + model = addProtein(model,uniprots{j}{k},kegg,swissprot); + end + newMets{k} = ['prot_' newMets{k}]; + end + %Add new protein stoich. coeffs to rxn: + kvalues = kcats(j)./stoich{j}; + rxnID = model.rxns{i}; + rxnName = model.rxnNames{i}; + model = addEnzymesToRxn(model,kvalues,rxnID,newMets,{rxnID,rxnName},grRule); + end + end + %Update int_pos: + S = model.S; + subs_pos = find(S(:,i) < 0); + %Get the proteins that are part of the i-th rxn + prot_pos = find(~cellfun(@isempty,strfind(model.mets,'prot_'))); + int_pos = intersect(subs_pos,prot_pos)'; + %%%%%%%%%%%%%%%%%%%%%%%%%%% Individual Changes: %%%%%%%%%%%%%%%%%%%%%%%% + for j = 1:length(int_pos) + enzName = model.mets(int_pos(j)); + %%%%%%%%%%%%%%%%%% MANUAL CURATION FOR TOP GROWTH LIMITING ENZYMES: + [newValue,modifications] = curation_growthLimiting(reaction,enzName,MW_set,modifications); + if ~isempty(newValue) + model.S(int_pos(j),i) = newValue; + else + %%%%%%%%%%%%%%%%%%%% MANUAL CURATION FOR CARBON SOURCES + [newValue,modifications] = curation_carbonSources(reaction,enzName,MW_set,modifications); + if ~isempty(newValue) + model.S(int_pos(j),i) = newValue; + else + %%%%%%%%%%%%%%% MANUAL CURATION FOR TOP USED ENZYMES: + [newValue,modifications] = curation_topUsedEnz(reaction,enzName,MW_set,modifications); + if ~isempty(newValue) + model.S(int_pos(j),i) = newValue; + end + end + end + end + disp(['Improving model with curated data: Ready with rxn #' num2str(i)]) +end + +%%%%%%%%%%%%%%%%%%%%%%%%% Other manual changes: %%%%%%%%%%%%%%%%%%%%%%%%%%% +model = otherChanges(model); +% Remove repeated reactions (2017-01-16): +rem_rxn = false(size(model.rxns)); +for i = 1:length(model.rxns)-1 + for j = i+1:length(model.rxns) + if isequal(model.S(:,i),model.S(:,j)) && model.lb(i) == model.lb(j) && ... + model.ub(i) == model.ub(j) + rem_rxn(j) = true; + disp(['Removing repeated rxn: ' model.rxns{i} ' & ' model.rxns{j}]) + end + end +end +model = removeReactions(model,model.rxns(rem_rxn),true); +% Merge arm reactions to reactions with only one isozyme (2017-01-17): +arm_pos = zeros(size(model.rxns)); +p = 0; +for i = 1:length(model.rxns) + rxn_id = model.rxns{i}; + if contains(rxn_id,'arm_') + rxn_code = rxn_id(5:end); + k = 0; + for j = 1:length(model.rxns) + if ~isempty(strfind(model.rxns{j},[rxn_code 'No'])) + k = k + 1; + pos = j; + grRule = model.grRules{j}; + end + end + if k == 1 + %Condense both reactions in one: + model = addReaction(model,model.rxns{pos}, ... + 'reactionName', model.rxnNames{pos}, ... + 'metaboliteList', model.mets, ... + 'stoichCoeffList', model.S(:,i) + model.S(:,pos), ... + 'lowerBound', 0, ... + 'upperBound', 1000); + model.grRules{pos} = grRule; + p = p + 1; + arm_pos(p) = i; + disp(['Merging reactions: ' model.rxns{i} ' & ' model.rxns{pos}]) + end + end +end +% Remove saved arm reactions: +model = removeReactions(model,model.rxns(arm_pos(1:p)),true); + +% Remove unused enzymes after manual curation (2017-01-16): +rem_enz = false(size(model.enzymes)); +for i = 1:length(model.enzymes) + pos_met = strcmp(model.mets,['prot_' model.enzymes{i}]); + if sum(model.S(pos_met,:)~=0) == 1 + rem_enz(i) = true; + end +end +rem_enz = model.enzymes(rem_enz); +for i = 1:length(rem_enz) + model = deleteProtein(model,rem_enz{i}); + disp(['Removing unused protein: ' rem_enz{i}]) +end + +% Block O2 and glucose production (for avoiding multiple solutions): +model.ub(strcmp(model.rxnNames,'oxygen exchange')) = 0; +model.ub(strcmp(model.rxnNames,'D-glucose exchange')) = 0; + +% Remove incorrect pathways: +model = removeIncorrectPathways(model); + +% Map the index of the modified Kcat values to the new model (after rxns removals) +modifications = mapModifiedRxns(modifications,model); + +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Modify the top growth limiting enzymes that were detected by the +% modifyKcats.m script in a preliminary run. +function [newValue,modifications] = curation_growthLimiting(reaction,enzName,MW_set,modifications) + newValue = []; + reaction = string(reaction); + % Ketol-acid Reductoisomerase (P06168/EC1.1.1.86) - 2-acetyllactic + % acid: Substrate name in BRENDA was a synonim as name in model. + % Changed manually (2015-08-28). + if strcmpi('prot_P06168',enzName) + if contains(reaction,'acetohydroxy acid isomeroreductase (') + newValue = -(18.3*3600)^-1; %BRENDA: 2-acetolactate + modifications{1} = [modifications{1}; 'P06168']; + modifications{2} = [modifications{2}; reaction]; + end + end + % HMG-CoA reductase (P12683-P12684/EC1.1.1.34): Only kcat available + % in BRENDA was for Rattus Norvegicus. Value corrected with a + % s.a. from the Haloferax volcanii enzyme expressed in E. coli (max + % value available in BRENDA for a microorganism, without counting + % recombinant proteins) (2018-08-11). Ref: Bischoff & Rodwell 1996 + % https://www.ncbi.nlm.nih.gov/pmc/articles/PMC177616/ + if (strcmpi('prot_P12683',enzName)||strcmpi('prot_P12684',enzName)) && ... + contains(reaction,'hydroxymethylglutaryl') + newValue = -(24*60*1e3/1e3*MW_set)^-1; + modifications{1} = [modifications{1}; 'P12683']; + modifications{2} = [modifications{2}; reaction]; + end + % [1.2.1.12] Kcat (Sce & natural substrate) 29 [1/s] is a highly + % growthRate limiting value, S.A. 100 (Sce) + %enzIDs = {'prot_P00359','prot_P00360','prot_P00358'}; + if contains(reaction,'glyceraldehyde-3-phosphate dehydrogenase') + if strcmpi('prot_P00360',enzName) + newValue = -(29*3600)^-1; + %newValue = -(100*1e3/1e3*MW_set)^-1; + modifications{1} = [modifications{1}; 'P00360']; + modifications{2} = [modifications{2}; reaction]; + elseif strcmpi('prot_P00358',enzName) + newValue = -(16.7*3600)^-1; + %newValue = -(100*1e3/1e3*MW_set)^-1; + modifications{1} = [modifications{1}; 'P00358']; + modifications{2} = [modifications{2}; reaction]; + elseif strcmpi('prot_P00359',enzName) + newValue = -(9.1*3600)^-1; + %newValue = -(100*1e3/1e3*MW_set)^-1; + modifications{1} = [modifications{1}; 'P00359']; + modifications{2} = [modifications{2}; reaction]; + end + end + % [4.1.1.-, 4.1.1.43, 4.1.1.72, 4.1.1.74] Pyruvate decarboxylase + % Resulted to be a growth limiting enzyme but the Kcat + % value seems to be the best candidate for this reaction (rev) + if strcmpi('prot_P06169',enzName) && contains(reaction,'pyruvate decarboxylase') + %newValue = -1/(73.1*3600); + newValue = -1/(145*3600); + modifications{1} = [modifications{1}; 'P06169']; + modifications{2} = [modifications{2}; reaction]; + end + % [Q00955//EC6.4.1.2] Acetyl-CoA carboxylase + % Assigned value was 1.23 (1/s), S.A. was used instead (6 + % umol/min/mg) from BRENDA (2018-01-22). + if strcmpi('prot_Q00955',enzName) && contains(reaction,'acetyl-CoA carboxylase') + newValue = -(6*60*1e3/1e3*MW_set)^-1; + modifications{1} = [modifications{1}; 'Q00955']; + modifications{2} = [modifications{2}; reaction]; + end + % [P52867//EC2.4.1.109] mannosyltransferase + % Assigned value was 0.0053 (1/s) for Sce, however this value yields + % a growth control coefficient 0f 0.9316. The highest kinetic + % parameter reported for this EC number is the one that was + % assigned, a wild card is introduced into the EC number and the highest + % value for a similar metabolite was chosen instead: + % 70.9 [1/s] reported for EC2.4.1.83 in Saccharomyces cerevisiae + % from BRENDA (2018-04-10). + if strcmpi('prot_P52867',enzName) && contains(reaction,'dolichyl-phosphate-mannose--protein mannosyltransferase') + newValue = -(70.9*3600)^-1; + modifications{1} = [modifications{1}; 'P52867']; + modifications{2} = [modifications{2}; reaction]; + end + % [P52867//EC2.5.1.6] methionine adenosyltransferase. Highly growth + % limiting enzyme. No reported value for S. cerevisiae, Kcat for + % Rattus norvegicus used instead <- 0.583 [1/s]. + % from BRENDA (2018-04-10). + if strcmpi('prot_P10659',enzName) && contains(reaction,'methionine adenosyltransferase') + newValue = -(0.583*3600)^-1; + modifications{1} = [modifications{1}; 'P10659']; + modifications{2} = [modifications{2}; reaction]; + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Modify those kcats involved in extreme misspredictions for growth on +% several carbon sources. This values were obtained by specific searches on +% the involved pathways for the identification of the ec numbers and then +% its associated Kcat values were gotten from BRENDA. +function [newValue,modifications] = curation_carbonSources(reaction,enzName,MW_set,modifications) + newValue = []; + reaction = string(reaction); + % alpha,alpha-trehalase (P32356/EC3.2.1.28): The growth on + % alpha,alpha-trehalose was extremely highly overpredicted, the + % minimum substrate specific kcat value (0.67 1/s) is chosen instead + % (Metarhizium flavoviride) + % from BRENDA (2018-01-22). + if (strcmpi('prot_P32356',enzName) || strcmpi('prot_P48016',enzName)) && ... + contains(reaction,'alpha,alpha-trehalase') + newValue = -(0.67*60*1e3/1e3*MW_set)^-1; + modifications{1} = [modifications{1}; 'P48016']; + modifications{2} = [modifications{2}; reaction]; + end + % alcohol dehydrogenase (ethanol to acetaldehyde)[P00331/EC1.1.1.1] + % Growth on ethanol was extremely highly overpredicted, the + % specific Kcat value for ethanol found in BRENDA is 143 (1/s) @pH + % 8.0 and 25 C, and 432.3 @20?C and pH 9.0 + % from BRENDA (2018-01-22). + if strcmpi('prot_P00331',enzName) && contains(reaction,'alcohol dehydrogenase (ethanol to acetaldehyde)') + newValue = -(143*3600)^-1; + modifications{1} = [modifications{1}; 'P00331']; + modifications{2} = [modifications{2}; reaction]; + end + % Glycerol dehydrogenase [P14065/EC1.1.1.1] + % Growth on glycerol was highly overpredicted, the minimum S.A. + % value for glycerol found in BRENDA is 0.54 (umol/ming/mg) and + % the maximal is 15.7, both for Schizosaccharomyces pombe. The MW + % is 57 kDa. From BRENDA (2018-01-26). + if strcmpi('prot_P14065',enzName) && contains(reaction,'glycerol dehydrogenase (NADP-dependent)') + newValue = -(0.54*57000*0.06)^-1; + modifications{1} = [modifications{1}; 'P14065']; + modifications{2} = [modifications{2}; reaction]; + end + % alpha-glucosidase [EC3.2.1.10/EC3.2.1.20] + % Growth on maltose is still underpredicted, for this reason all + % the isoenzymes catalysing its conversion are set up to the + % maximal glucosidase rate for maltose (3.2.1.20) <- 709 1/s for + % Schizosaccharomyces pombe, a value of 1278 is also available for + % Sulfolobus acidocaldarius. + % From BRENDA (2018-01-28). + if contains(reaction,'alpha-glucosidase') + newValue = -(1278*3600)^-1; + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% After the growth limiting Kcats analysis and the curation for several +% carbon sources, a simulation for the model growing on minimal glucose +% media yielded a list of the top used enzymes (mass-wise), those that were +% taking more than 10% of the total proteome are chosen for manual curation +function [newValue,modifications] = curation_topUsedEnz(reaction,enzName,MW_set,modifications) + newValue = []; + reaction = string(reaction); + % amidophosphoribosyltransferase [P04046/EC2.4.2.14] + % No Kcat reported on BRENDA, the maximum S.A. (E. coli is taken + % instead) + if strcmpi('prot_P04046',enzName) && contains(reaction,'phosphoribosylpyrophosphate amidotransferase') + newValue = -(17.2*194000*60/1000)^-1; + modifications{1} = [modifications{1}; 'P04046']; + modifications{2} = [modifications{2}; reaction]; + end + % Glutamate N-acetyltransferase (Q04728/EC2.3.1.1): Missanotated + % EC (should be 2.3.1.35), and no kcats for the correct EC. were + % found. Value corrected with s.a. in S.cerevisiae + % from BRENDA (2015-08-31). + if strcmpi('prot_Q04728',enzName) && contains(reaction,'ornithine transacetylase (No1)') + newValue = -(22*60*1e3/1e3*MW_set)^-1; %22 [umol/min/mg] + modifications{1} = [modifications{1}; 'Q04728']; + modifications{2} = [modifications{2}; reaction]; + end + % atp synthase mitochondrial (P07251-P00830/EC3.6.3.14): No Kcat + % reported for S. cerevisiae, value for Bacillus sp. + % for ATP is used instead 217 [1/s] + % from BRENDA (2017-01-18). + if (strcmpi('prot_P07251',enzName) || strcmpi('prot_P00830',enzName))&& ... + contains(reaction,'ATP synthase (No1)') + newValue = -(390*3600)^-1; + modifications{1} = [modifications{1}; 'P07251']; + modifications{2} = [modifications{2}; reaction]; + end + % transaldolase (reversible) (P15019/EC2.2.1.2): + % The protein usage is represents around 10% of the used proteome + % on several carbon sources (batch simulations). The highest S.A. + % was used instead (60*75000*60/1000) [1/hr] for E. coli + % from BRENDA (2017-01-18). + if strcmpi('prot_P15019',enzName) && contains(reaction,'transaldolase (reversible)') + newValue = -(60*75000*60/1000)^-1; + modifications{1} = [modifications{1}; 'P15019']; + modifications{2} = [modifications{2}; reaction]; + end + % [Q06817//EC6.1.1.14] glycyl-tRNA synthetase + % Resulted to be a growth limiting enzyme, maximum catalytic value + % found by the automatic algorithm was 15.9 1/s + % from BRENDA (2018-01-22). + if strcmpi('prot_Q06817',enzName) && contains(reaction,'glycyl-tRNA synthetase') + newValue = -1/(15.9*3600); + modifications{1} = [modifications{1}; 'Q06817']; + modifications{2} = [modifications{2}; reaction]; + end + % P80235/EC 2.3.1.7 - carnitine O-acetyltransferase + % The assigned Kcat was for Mus musculus and resulted to take + % 20% of the used proteome on batch simulation for several + % carbon sources. Sce S.A. is used instead [200 umol/min/mg] + % from BRENDA (2018-01-18) + if strcmpi('prot_P80235',enzName) && contains(reaction,'carnitine O-acetyltransferase') + newValue = -(200*60*1e3/1e3*MW_set)^-1; %22 [umol/min/mg] + modifications{1} = [modifications{1}; 'P80235']; + modifications{2} = [modifications{2}; reaction]; + end + % FAS (P07149+P19097/EC2.3.1.86): No kcat available in BRENDA + % (it was using ECs of subunits: EC3.1.2.14 and EC2.3.1.41).Value + % corrected with max. s.a. in S.cerevisiae for NADPH in BRENDA + % (2015-08-25) + if (strcmpi('prot_P07149',enzName) || strcmpi('prot_P19097',enzName)) + if contains(reaction,'fatty-acyl-CoA synthase (n-C16:0CoA)') + newValue = -(3/14*60*1e3/1e3*MW_set)^-1; %3/14 [umol/min/mg] + elseif contains(reaction,'fatty-acyl-CoA synthase (n-C18:0CoA)') + newValue = -(3/16*60*1e3/1e3*MW_set)^-1; %3/16 [umol/min/mg] + end + modifications{1} = [modifications{1}; 'P07149']; + modifications{2} = [modifications{2}; reaction]; + modifications{1} = [modifications{1}; 'P19097']; + modifications{2} = [modifications{2}; reaction]; + end + % Enolase (1&2) [4.2.1.11] 71.4 (1/s) is the Kcat reported for + % 2-phospho-D-glycerate + % 230 (1/s) is the Kcat reported for 2-phosphoglycerate + % both measurements are for native enzymes + if strcmpi('prot_P00924',enzName) && contains(reaction,'enolase') + newValue = -1/(230*3600); + modifications{1} = [modifications{1}; 'P00924']; + modifications{2} = [modifications{2}; reaction]; + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function model = otherChanges(model) + % Remove protein P14540 (Fructose-bisphosphate aldolase) from missanotated rxns + % (2017-01-16): + rxns_tochange = {'D-fructose 1-phosphate D-glyceraldehyde-3-phosphate-lyase (No1)';... + 'sedoheptulose 1,7-bisphosphate D-glyceraldehyde-3-phosphate-lyase (No1)';... + 'D-fructose 1-phosphate D-glyceraldehyde-3-phosphate-lyase (reversible) (No1)';... + 'sedoheptulose 1,7-bisphosphate D-glyceraldehyde-3-phosphate-lyase (reversible) (No1)'}; + for i = 1:length(rxns_tochange) + rxn_name = rxns_tochange{i}; + pos_rxn = find(strcmpi(model.rxnNames,rxn_name)); + rxn_id = model.rxns{pos_rxn}; + model.S(strcmpi(model.mets,'prot_P14540'),pos_rxn) = 0; + model.rxns{pos_rxn} = rxn_id(1:end-3); + end + + % Remove protein P40009 (Golgi apyrase) from missanotated rxns (2016-12-14): + pos_rxn = find(~cellfun(@isempty,strfind(model.rxnNames,'ATPase, cytosolic (No3)'))); + if ~isempty(pos_rxn) && pos_rxn~=0 + model = removeReactions(model,model.rxns(pos_rxn)); + end + + % Remove 2 proteins from missanotated rxns: Q12122 from cytosolic rxn (it's + % only mitochondrial) & P48570 from mitochondrial rxn (it's only cytosolic). + % Also rename r_0543No2 to r_0543No1 (for consistency) (2017-08-28): + model = removeReactions(model,{'r_0543No1'}); + model = removeReactions(model,{'r_1838No2'}); + index = find(strcmp(model.rxns,'r_0543No2')); + if ~isempty(index) + model.rxnNames{index} = 'homocitrate synthase (No1)'; + model.rxns{index} = 'r_0543No1'; + end + % Aconitase (P19414/EC 4.2.1.3): The rxn is represented as a two + % step rxn in Yeast7.5, so the kcat must be multiplied by 2 + %(2015-11-05) + index = find(strcmpi('prot_P19414',model.mets)); + if ~isempty(index) + rxnIndxs = find(model.S(:,index)); + if ~isempty(rxnIndxs) + rxnIndxs = rxnIndxs(1:end-2); + model.S(rxnIndxs,index) = model.S(rxnIndxs,index)/2; + end + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function modified = mapModifiedRxns(modifications,model) + modified = []; + for i=1:length(modifications{1}) + rxnIndex = find(strcmp(model.rxnNames,modifications{2}(i)),1); + str = {horzcat(modifications{1}{i},'_',num2str(rxnIndex))}; + modified = [modified; str]; + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% From a8763546f045327db4a52b87d1cb0235ce84f10c Mon Sep 17 00:00:00 2001 From: Eduard Kerkhoven Date: Fri, 5 Oct 2018 10:35:03 +0200 Subject: [PATCH 02/14] refactor: findInDB/getEnzymeCodes speed-up - instead of reorganizing DBs for each rxn iteration, assign relevant KEGG and SwissProt fields in beginning of `getEnzymeCodes` - flatten DBgenes field with auxiliary function `flattenCell` for faster `findInDB` (`flattenCell` will be part of RAVEN in near future) --- geckomat/get_enzyme_data/findInDB.m | 357 ++++++++++--------- geckomat/get_enzyme_data/flattenCell.m | 43 +++ geckomat/get_enzyme_data/getEnzymeCodes.m | 408 +++++++++++----------- 3 files changed, 429 insertions(+), 379 deletions(-) create mode 100644 geckomat/get_enzyme_data/flattenCell.m diff --git a/geckomat/get_enzyme_data/findInDB.m b/geckomat/get_enzyme_data/findInDB.m index ee0775af3..bb13f58c6 100644 --- a/geckomat/get_enzyme_data/findInDB.m +++ b/geckomat/get_enzyme_data/findInDB.m @@ -1,181 +1,178 @@ -function [uni,EC,MW,Genes,conflicts] = findInDB(grRule,DB) -% findInDB -% Gets the uniprots and EC numbers for a given rxn into a given database -% -% grRule Genes association for a given metabolic reaction -% DB Cell array containing genes-> protein relationships, EC#s,MWs -% and sequences coming from external databases (Swissprot or -% KEGG) -% -% uni Array containing all the uniprot IDs related to an isoenzyme, -% for a given reaction in each of its cells. -% EC Array containing all the EC numbers related to an isoenzyme, -% for a given reaction in each of its cells. -% Genes Array containing all the genes related to an isoenzyme, -% for a given reaction in each of its cells. -% conflicts Contains those genes with multiple protein matches. -% -% Usage: [uni,EC,MW,Genes,conflicts] = findInDB(grRule,DB,action) -% -% Benjamin J. Sanchez, 2017-08-10 -% Ivan Domenzain, 2018-09-06 -% - -%Find simple gene sets for reaction: -gene_sets = getSimpleGeneSets(grRule); -%Preallocate function variables -uni = cell(size(gene_sets)); -EC = cell(size(gene_sets)); -MW = zeros(size(gene_sets)); -Genes = cell(size(gene_sets)); -DBgenes = DB(:,3); -DBecNum = DB(:,4); -conflicts = cell(1,2); - -for i = 1:length(gene_sets) - %Split the gene set and match each gene: - gene_set = strsplit(gene_sets{i},' and '); - uni_set = cell(size(gene_set)); - EC_set = cell(size(gene_set)); - genesCell = cell(size(gene_set)); - for j = 1:length(gene_set) - gene = gene_set{j}; - %Get the indexes for all of the proteins related to gene - matches = cellfun(@(x) sum(strcmpi(x,gene)), DBgenes,'UniformOutput',false); - matches = (find(cell2mat(matches))); - if ~isempty(matches) - uni_set{j} = DB{matches,1}; - genesCell{j} = gene; - %Get the indexes of the matched protein(s) with non-empty EC#s - nonEmpty = matches(~cellfun(@isempty,DBecNum(matches))); - if ~isempty(nonEmpty) - [geneECs,ia] = unique(DBecNum(nonEmpty),'stable'); - %For genes with multiple proteins associated to several - %non-empty ecNumbers - if length(geneECs)>1 - indexes = nonEmpty(ia); - ecNum = DBecNum(indexes); - %Save first match - uni_set{j} = DB{indexes(1),1}; - EC_set{j} = getECstring(EC_set{j},ecNum{1}); - %Save additional matches as potential conflicts - if ~ismember(gene,conflicts{1}) - conflicts{1} = [conflicts{1}; {gene}]; - conflicts{2} = [conflicts{2}; {indexes}]; - end - else - %If there is a single unique ec number for the matched - %protein(s), then choose the lightest protein - [~,minW] = min(cell2mat(DB(nonEmpty,5))); - matches = nonEmpty(minW); - uni_set{j} = DB{matches,1}; - EC_set{j} = getECstring(EC_set{j},DBecNum{matches}); - end - end - else - uni_set{j} = ''; - end - - if isempty(EC_set{j}) - EC_set{j} = ''; - end - end - %Uniprot: Delete repeated and empty spaces - [uni_set,repeated] = deleteRepeated(uni_set); - genesCell = genesCell(~repeated); - emptyIndexes = cellfun(@isempty,uni_set); - uni_set = uni_set(~emptyIndexes); - genesCell = genesCell(~emptyIndexes); - - %EC: Find union and intersection between all units (only applies for - %complexes, i.e. length(EC_set) > 1): - uni_EC = strsplit(EC_set{1},' '); - int_EC = uni_EC; - for j = 2:length(EC_set) - other_EC = strsplit(EC_set{j},' '); - if isempty(uni_EC) - uni_EC = other_EC; - int_EC = other_EC; - elseif ~isempty(other_EC) - uni_EC = compare_wild([uni_EC other_EC]); - int_EC = intersection(int_EC,other_EC); - end - end - %Use the intersection found, if any. If not, use the union: - if isempty(int_EC) - EC_set = uni_EC; - else - EC_set = int_EC; - end - %MW: use uniprot codes + swissprot table: - for j = 1:length(uni_set) - index = strcmpi(DB(:,1),uni_set{j}); - minWeight = min(DB{index,5}); - MW(i) = MW(i) + minWeight; - end - %Add new codes as new possible isoenzymes: - uni{i} = strjoin(uni_set,' '); - EC{i} = strjoin(EC_set,' '); - Genes{i} = strjoin(genesCell,' '); -end - -%Delete repeated Uniprots (and the corresponding ECs): -[uni,deleted] = deleteRepeated(uni); -EC(deleted) = []; -MW(deleted) = []; - -end - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -function int_EC = intersection(prev_EC,new_EC) -%Finds the common elements between two cell arrays, if any. Also considers -%wildcards (e.g. if 'EC1.1.1.1' is in one array and 'EC1.1.1.-' is in the -%other one, then 'EC1.1.1.1' is added to the intersection). -int_EC = {}; -for i = 1:length(prev_EC) - for j = 1:length(new_EC) - new_int = compare_wild({prev_EC{i} new_EC{j}}); - if length(new_int) == 1 - int_EC = [int_EC new_int]; - end - end -end -end -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -function int_EC = compare_wild(EC) -%Goes through a cell array of EC numbers, and erases any repetitions, -%considering also wildcards (e.g. will erase 'EC1.2.3.-' if 'EC1.2.3.4' is -%already present). - -%Trim all EC numbers of wild cards (e.g. 'EC1.2.-.-' -> 'EC1.2.'): -EC_trimmed = EC; -for i = 1:length(EC) - %Add a final dot for avoiding issues (e.g. 'EC1.1.1.1' & 'EC1.1.1.12'): - ECi = [EC{i} '.']; - pos = strfind(ECi,'-'); - if ~isempty(pos) - ECi = ECi(1:pos(1)-1); - end - EC_trimmed{i} = ECi; -end - -%Compare all EC numbers between them to find if 1 fits in the other: -non_repeated = true(1,length(EC)); -for i = 1:length(EC)-1 - for j = i+1:length(EC) - ECi = EC_trimmed{i}; - ECj = EC_trimmed{j}; - %If ECj fits in ECi then ECj can be disregarded: - if strfind(ECi,ECj) - non_repeated(j) = false; - %Else, if ECi fits in ECj then ECi can be disregarded: - elseif strfind(ECj,ECi) - non_repeated(i) = false; - end - end -end - -int_EC = EC(non_repeated); - -end +function [uni,EC,MW,Genes,conflicts] = findInDB(grRule,DBprot,DBgenes,DBecNum,DBMW) +% findInDB +% Gets the uniprots and EC numbers for a given rxn into a given database +% +% grRule Genes association for a given metabolic reaction +% DB Cell array containing genes-> protein relationships, EC#s,MWs +% and sequences coming from external databases (Swissprot or +% KEGG) +% +% uni Array containing all the uniprot IDs related to an isoenzyme, +% for a given reaction in each of its cells. +% EC Array containing all the EC numbers related to an isoenzyme, +% for a given reaction in each of its cells. +% Genes Array containing all the genes related to an isoenzyme, +% for a given reaction in each of its cells. +% conflicts Contains those genes with multiple protein matches. +% +% Usage: [uni,EC,MW,Genes,conflicts] = findInDB(grRule,DB,action) +% +% Benjamin J. Sanchez, 2017-08-10 +% Ivan Domenzain, 2018-09-06 +% + +%Find simple gene sets for reaction: +gene_sets = getSimpleGeneSets(grRule); +%Preallocate function variables +uni = cell(size(gene_sets)); +EC = cell(size(gene_sets)); +MW = zeros(size(gene_sets)); +Genes = cell(size(gene_sets)); +conflicts = cell(1,2); + +for i = 1:length(gene_sets) + %Split the gene set and match each gene: + gene_set = strsplit(gene_sets{i},' and '); + uni_set = cell(size(gene_set)); + EC_set = cell(size(gene_set)); + genesCell = cell(size(gene_set)); + for j = 1:length(gene_set) + gene = gene_set{j}; + %Get the indexes for all of the proteins related to gene + matches=find(sum(strcmpi(gene,DBgenes),2)); + if ~isempty(matches) + uni_set{j} = DBprot{matches}; + genesCell{j} = gene; + %Get the indexes of the matched protein(s) with non-empty EC#s + nonEmpty = matches(~cellfun(@isempty,DBecNum(matches))); + if ~isempty(nonEmpty) + [geneECs,ia] = unique(DBecNum(nonEmpty),'stable'); + %For genes with multiple proteins associated to several + %non-empty ecNumbers + if length(geneECs)>1 + indexes = nonEmpty(ia); + ecNum = DBecNum(indexes); + %Save first match + uni_set{j} = DBprot{indexes(1)}; + EC_set{j} = getECstring(EC_set{j},ecNum{1}); + %Save additional matches as potential conflicts + if ~ismember(gene,conflicts{1}) + conflicts{1} = [conflicts{1}; {gene}]; + conflicts{2} = [conflicts{2}; {indexes}]; + end + else + %If there is a single unique ec number for the matched + %protein(s), then choose the lightest protein + [~,minW] = min(cell2mat(DBMW(nonEmpty))); + matches = nonEmpty(minW); + uni_set{j} = DBprot{matches}; + EC_set{j} = getECstring(EC_set{j},DBecNum{matches}); + end + end + else + uni_set{j} = ''; + end + + if isempty(EC_set{j}) + EC_set{j} = ''; + end + end + %Uniprot: Delete repeated and empty spaces + [uni_set,repeated] = deleteRepeated(uni_set); + genesCell = genesCell(~repeated); + emptyIndexes = cellfun(@isempty,uni_set); + uni_set = uni_set(~emptyIndexes); + genesCell = genesCell(~emptyIndexes); + + %EC: Find union and intersection between all units (only applies for + %complexes, i.e. length(EC_set) > 1): + uni_EC = strsplit(EC_set{1},' '); + int_EC = uni_EC; + for j = 2:length(EC_set) + other_EC = strsplit(EC_set{j},' '); + if isempty(uni_EC) + uni_EC = other_EC; + int_EC = other_EC; + elseif ~isempty(other_EC) + uni_EC = compare_wild([uni_EC other_EC]); + int_EC = intersection(int_EC,other_EC); + end + end + %Use the intersection found, if any. If not, use the union: + if isempty(int_EC) + EC_set = uni_EC; + else + EC_set = int_EC; + end + %MW: use uniprot codes + swissprot table: + for j = 1:length(uni_set) + index = strcmpi(DBprot(:),uni_set{j}); + minWeight = min(DBMW{index}); + MW(i) = MW(i) + minWeight; + end + %Add new codes as new possible isoenzymes: + uni{i} = strjoin(uni_set,' '); + EC{i} = strjoin(EC_set,' '); + Genes{i} = strjoin(genesCell,' '); +end + +%Delete repeated Uniprots (and the corresponding ECs): +[uni,deleted] = deleteRepeated(uni); +EC(deleted) = []; +MW(deleted) = []; + +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function int_EC = intersection(prev_EC,new_EC) +%Finds the common elements between two cell arrays, if any. Also considers +%wildcards (e.g. if 'EC1.1.1.1' is in one array and 'EC1.1.1.-' is in the +%other one, then 'EC1.1.1.1' is added to the intersection). +int_EC = {}; +for i = 1:length(prev_EC) + for j = 1:length(new_EC) + new_int = compare_wild({prev_EC{i} new_EC{j}}); + if length(new_int) == 1 + int_EC = [int_EC new_int]; + end + end +end +end +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function int_EC = compare_wild(EC) +%Goes through a cell array of EC numbers, and erases any repetitions, +%considering also wildcards (e.g. will erase 'EC1.2.3.-' if 'EC1.2.3.4' is +%already present). + +%Trim all EC numbers of wild cards (e.g. 'EC1.2.-.-' -> 'EC1.2.'): +EC_trimmed = EC; +for i = 1:length(EC) + %Add a final dot for avoiding issues (e.g. 'EC1.1.1.1' & 'EC1.1.1.12'): + ECi = [EC{i} '.']; + pos = strfind(ECi,'-'); + if ~isempty(pos) + ECi = ECi(1:pos(1)-1); + end + EC_trimmed{i} = ECi; +end + +%Compare all EC numbers between them to find if 1 fits in the other: +non_repeated = true(1,length(EC)); +for i = 1:length(EC)-1 + for j = i+1:length(EC) + ECi = EC_trimmed{i}; + ECj = EC_trimmed{j}; + %If ECj fits in ECi then ECj can be disregarded: + if strfind(ECi,ECj) + non_repeated(j) = false; + %Else, if ECi fits in ECj then ECi can be disregarded: + elseif strfind(ECj,ECi) + non_repeated(i) = false; + end + end +end + +int_EC = EC(non_repeated); + +end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \ No newline at end of file diff --git a/geckomat/get_enzyme_data/flattenCell.m b/geckomat/get_enzyme_data/flattenCell.m new file mode 100644 index 000000000..1429ae4fa --- /dev/null +++ b/geckomat/get_enzyme_data/flattenCell.m @@ -0,0 +1,43 @@ +function Cflat = flattenCell(C,strFlag) +%FLATTENCELL Flatten a nested column cell array into a matrix cell array. +% +% CFLAT = FLATTENCELL(C) takes a column cell array in which one or more +% entries is a nested cell array, and flattens it into a 2D matrix cell +% array, where the nested entries are spread across new columns. +% +% CFLAT = FLATTENCELL(C,STRFLAG) if STRFLAG is TRUE, empty entries in the +% resulting CFLAT will be replaced with empty strings {''}. Default = FALSE +% +% +% Jonathan Robinson, 2018-03-07 + + +if nargin < 2 + strFlag = false; +end + +% determine which entries are cells +cells = cellfun(@iscell,C); + +% determine number of elements in each nested cell +cellsizes = cellfun(@numel,C); +cellsizes(~cells) = 1; % ignore non-cell entries + +% flatten single-entry cells +Cflat = C; +Cflat(cells & (cellsizes == 1)) = cellfun(@(x) x{1},Cflat(cells & (cellsizes == 1)),'UniformOutput',false); + +% iterate through multi-entry cells +multiCells = find(cellsizes > 1); +for i = 1:length(multiCells) + cellContents = Cflat{multiCells(i)}; + Cflat(multiCells(i),1:length(cellContents)) = cellContents; +end + +% change empty elements to strings, if specified +if ( strFlag ) + Cflat(cellfun(@isempty,Cflat)) = {''}; +end + + + diff --git a/geckomat/get_enzyme_data/getEnzymeCodes.m b/geckomat/get_enzyme_data/getEnzymeCodes.m index f68ecb3da..f8c6eebde 100644 --- a/geckomat/get_enzyme_data/getEnzymeCodes.m +++ b/geckomat/get_enzyme_data/getEnzymeCodes.m @@ -1,206 +1,216 @@ -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% model_data = getEnzymeCodes(model,action) -% Retrieves the enzyme codes for each of the reactions for a given genome -% scale model (GEM). -% -% INPUT: *model: a GEM file (.mat format) -% *action: Response action if multiple proteins with -% different EC numbers are found for a given gene in -% a metabolic reaction. -% - 'display' Displays all found multiplicities. -% - 'ignore' Ignore multiplicities and use the -% protein with the lowest index in the database. -% - 'add' Adds all the multiple proteins as -% isoenzymes for the given reaction. -% -% OUTPUTS: model_data, which contains: -% *model: The standardized GEM -% *substrates: Substrates associated for each rxn -% *products: Products associated, when rxn is reversible -% *uniprots: All possible uniprot codes, for each rxn -% *EC_numbers: All possible EC numbers, for each uniprot -% *count(1): #rxns with data from Swissprot -% *count(2): #rxns with data from KEGG -% *count(3): #exchange/transport rxns with no GPRs -% *count(4): #other rxns -% -% Benjamin Sanchez. Last edited: 2017-03-05 -% Ivan Domenzain. Last edited: 2018-09-07 -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -function model_data = getEnzymeCodes(model,action) - -if nargin<2 - action = 'display'; -end - -%Standardize grRules to avoid wrong enzyme codes assignments to reactions -[grRules,~] = standardizeGrRules(model,true); -model.grRules = grRules; - -data = load('../../databases/ProtDatabase.mat'); -swissprot = data.swissprot; -kegg = data.kegg; - -swissprot = standardizeDatabase(swissprot); -kegg = standardizeDatabase(kegg); - -[m,n] = size(model.S); -substrates = cell(n,20); -products = cell(n,20); -uniprots = cell(n,20); -Genes = cell(n,20); -EC_numbers = cell(n,20); -MWs = zeros(n,20); -isrev = zeros(n,1); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% model_data = getEnzymeCodes(model,action) +% Retrieves the enzyme codes for each of the reactions for a given genome +% scale model (GEM). +% +% INPUT: *model: a GEM file (.mat format) +% *action: Response action if multiple proteins with +% different EC numbers are found for a given gene in +% a metabolic reaction. +% - 'display' Displays all found multiplicities. +% - 'ignore' Ignore multiplicities and use the +% protein with the lowest index in the database. +% - 'add' Adds all the multiple proteins as +% isoenzymes for the given reaction. +% +% OUTPUTS: model_data, which contains: +% *model: The standardized GEM +% *substrates: Substrates associated for each rxn +% *products: Products associated, when rxn is reversible +% *uniprots: All possible uniprot codes, for each rxn +% *EC_numbers: All possible EC numbers, for each uniprot +% *count(1): #rxns with data from Swissprot +% *count(2): #rxns with data from KEGG +% *count(3): #exchange/transport rxns with no GPRs +% *count(4): #other rxns +% +% Benjamin Sanchez. Last edited: 2017-03-05 +% Ivan Domenzain. Last edited: 2018-09-07 +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function model_data = getEnzymeCodes(model,action) + +if nargin<2 + action = 'display'; +end + +%Standardize grRules to avoid wrong enzyme codes assignments to reactions +[grRules,~] = standardizeGrRules(model,true); +model.grRules = grRules; + +data = load('../../databases/ProtDatabase.mat'); +swissprot = data.swissprot; +kegg = data.kegg; + +swissprot = standardizeDatabase(swissprot); +kegg = standardizeDatabase(kegg); + +DBprotSwissprot = swissprot(:,1); +DBgenesSwissprot = flattenCell(swissprot(:,3)); +DBecNumSwissprot = swissprot(:,4); +DBMWSwissprot = swissprot(:,5); + +DBprotKEGG = kegg(:,1); +DBgenesKEGG = flattenCell(kegg(:,3)); +DBecNumKEGG = kegg(:,4); +DBMWKEGG = kegg(:,5); + +[m,n] = size(model.S); +substrates = cell(n,20); +products = cell(n,20); +uniprots = cell(n,20); +Genes = cell(n,20); +EC_numbers = cell(n,20); +MWs = zeros(n,20); +isrev = zeros(n,1); count = zeros(4,1); rgmat = full(model.rxnGeneMat); conflicts = cell(1,4); for i = 1:n ks = 1; kp = 1; - dir = 0; - inv = 0; - DB = ''; - %Save the substrates and products (if rxn is reversible): - for j = 1:m - if model.S(j,i) < 0 && model.ub(i) > 0 - substrates{i,ks} = model.metNames{j}; - ks = ks+1; - dir = 1; - elseif model.S(j,i) > 0 && model.lb(i) < 0 - products{i,kp} = model.metNames{j}; - kp = kp+1; - inv = 1; - end - end - %isrev(i) = 0 if rxn is blocked, = 1 if non-reversible, and = 2 if - %reversible: - isrev(i) = dir + inv; - if ~isempty(model.grRules{i}) - %Find match in Swissprot: - [new_uni,new_EC,new_MW,newGene,multGenes] = findInDB(model.grRules{i},swissprot); - if ~isempty(union_string(new_EC)) - count(1) = count(1) + isrev(i); - DBase = 'swissprot'; - if ~isempty(multGenes{1}) - multGenes{3} = DBase; - end - else - %Find match in KEGG: - [new_uni,new_EC,new_MW,newGene,multGenes] = findInDB(model.grRules{i},kegg); - if ~isempty(union_string(new_EC)) - count(2) = count(2) + isrev(i); - DBase = 'kegg'; - if ~isempty(multGenes{1}) - multGenes{3} = DBase; - end - else - %Check if rxn is an exchange/transport rxn with no GPRs: - GPRs = sum(rgmat(i,:)); - rxn_name = lower(model.rxnNames{i}); - exchange = contains(rxn_name,'exchange'); - uptake = contains(rxn_name,'uptake'); - production = contains(rxn_name,'production'); - transport = contains(rxn_name,'transport'); - if (exchange || uptake || production || transport) && GPRs == 0 - count(3) = count(3) + isrev(i); - else - count(4) = count(4) + isrev(i); - end - end - end - - for j = 1:length(new_uni) - uniprots{i,j} = new_uni{j}; - Genes{i,j} = newGene{j}; - if isempty(new_EC{j}) - EC_numbers{i,j} = union_string(new_EC); - else - EC_numbers{i,j} = new_EC{j}; - end - MWs(i,j) = new_MW(j); - end - - if ~isempty(multGenes{1}) - %Rxn index - conflicts{1} = [conflicts{1};i]; - %Gene IDs - conflicts{2} = [conflicts{2};multGenes{1}]; - %Indexes in DB - conflicts{3} = [conflicts{3};multGenes{2}]; - %DB name - conflicts{4} = [conflicts{4};multGenes{3}]; - if strcmpi(action,'add') - if strcmpi(DBase,'swissprot') - [uni,EC,MW,Genes] = addMultipleMatches(uni,EC,MW,Genes,multGenes,swissprot); - elseif strcmpi(DBase,'KEGG') - [uni,EC,MW,Genes] = addMultipleMatches(uni,EC,MW,Genes,multGenes,kegg); - end - end - end - end - disp(['Getting enzyme codes: Ready with rxn ' int2str(i)]) -end -model_data.model = model; -model_data.substrates = substrates; -model_data.products = products; -model_data.matchedGenes = Genes; -model_data.uniprots = uniprots; -model_data.EC_numbers = EC_numbers; -model_data.MWs = MWs; -model_data.count = count; -%Display error message with the multiple gene-protein matches found -if strcmpi(action,'display') && ~isempty(conflicts{1}) - displayErrorMessage(conflicts,swissprot,kegg) -end - -end -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -function database = standardizeDatabase(database) -for i = 1:length(database) - database{i,3} = strsplit(database{i,3},' '); -end -end -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -function str = union_string(cell_array) -%Receives any 1xn cell array and returns the union of all non empty -%elements as a string -nonempty = ~cellfun(@isempty,cell_array); -str = strjoin(cell_array(nonempty)',' '); -end -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -function [uni,EC,MW,genes] = addMultipleMatches(uni,EC,MW,genes,conflicts,DB) -for i=1:length(conflicts{1}) - indexes = conflicts{2}{i}; - for j=2:length(indexes) - indx = indexes(j); - uni = [uni; DB{indx,1}]; - ECset = getECstring('',DB{indx,4}); - EC = [EC; {ECset}]; - MW = [MW; DB{indx,5}]; - genes = [genes; conflicts{1}{i}]; - end -end -end -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -function displayErrorMessage(conflicts,swissprot,kegg) -STR = '\n Some genes with multiple associated proteins were found, please'; -STR = [STR, ' revise case by case in the protDatabase.mat file:\n\n']; -for i=1:length(conflicts{1}) - if strcmpi(conflicts{4}{i},swissprot) - DB = swissprot; - else - DB = kegg; - end - proteins = DB(conflicts{3}{i},1); - STR = [STR, '- gene: ' conflicts{2}{i} ' Proteins: ' strjoin(proteins) '\n']; -end -STR = [STR, '\nIf a wrongly annotated case was found then call the ']; -STR = [STR, 'getEnzymeCodes.m function again with the option action']; -STR = [STR, '= ignore\n\n']; -STR = [STR, 'If the conflicting proteins are desired to be kept as isoenzymes']; -STR = [STR, ' then call the getEnzymeCodes.m function']; -STR = [STR, ' again with the option action = add\n']; -error(sprintf(STR)) -end + dir = 0; + inv = 0; + DB = ''; + %Save the substrates and products (if rxn is reversible): + for j = 1:m + if model.S(j,i) < 0 && model.ub(i) > 0 + substrates{i,ks} = model.metNames{j}; + ks = ks+1; + dir = 1; + elseif model.S(j,i) > 0 && model.lb(i) < 0 + products{i,kp} = model.metNames{j}; + kp = kp+1; + inv = 1; + end + end + %isrev(i) = 0 if rxn is blocked, = 1 if non-reversible, and = 2 if + %reversible: + isrev(i) = dir + inv; + if ~isempty(model.grRules{i}) + %Find match in Swissprot: + [new_uni,new_EC,new_MW,newGene,multGenes] = findInDB(model.grRules{i},DBprotSwissprot,DBgenesSwissprot,DBecNumSwissprot,DBMWSwissprot); + if ~isempty(union_string(new_EC)) + count(1) = count(1) + isrev(i); + DBase = 'swissprot'; + if ~isempty(multGenes{1}) + multGenes{3} = DBase; + end + else + %Find match in KEGG: + [new_uni,new_EC,new_MW,newGene,multGenes] = findInDB(model.grRules{i},DBprotKEGG,DBgenesKEGG,DBecNumKEGG,DBMWKEGG); + if ~isempty(union_string(new_EC)) + count(2) = count(2) + isrev(i); + DBase = 'kegg'; + if ~isempty(multGenes{1}) + multGenes{3} = DBase; + end + else + %Check if rxn is an exchange/transport rxn with no GPRs: + GPRs = sum(rgmat(i,:)); + rxn_name = lower(model.rxnNames{i}); + exchange = contains(rxn_name,'exchange'); + uptake = contains(rxn_name,'uptake'); + production = contains(rxn_name,'production'); + transport = contains(rxn_name,'transport'); + if (exchange || uptake || production || transport) && GPRs == 0 + count(3) = count(3) + isrev(i); + else + count(4) = count(4) + isrev(i); + end + end + end + + for j = 1:length(new_uni) + uniprots{i,j} = new_uni{j}; + Genes{i,j} = newGene{j}; + if isempty(new_EC{j}) + EC_numbers{i,j} = union_string(new_EC); + else + EC_numbers{i,j} = new_EC{j}; + end + MWs(i,j) = new_MW(j); + end + + if ~isempty(multGenes{1}) + %Rxn index + conflicts{1} = [conflicts{1};i]; + %Gene IDs + conflicts{2} = [conflicts{2};multGenes{1}]; + %Indexes in DB + conflicts{3} = [conflicts{3};multGenes{2}]; + %DB name + conflicts{4} = [conflicts{4};multGenes{3}]; + if strcmpi(action,'add') + if strcmpi(DBase,'swissprot') + [uni,EC,MW,Genes] = addMultipleMatches(uni,EC,MW,Genes,multGenes,swissprot); + elseif strcmpi(DBase,'KEGG') + [uni,EC,MW,Genes] = addMultipleMatches(uni,EC,MW,Genes,multGenes,kegg); + end + end + end + end + disp(['Getting enzyme codes: Ready with rxn ' int2str(i)]) +end +model_data.model = model; +model_data.substrates = substrates; +model_data.products = products; +model_data.matchedGenes = Genes; +model_data.uniprots = uniprots; +model_data.EC_numbers = EC_numbers; +model_data.MWs = MWs; +model_data.count = count; +%Display error message with the multiple gene-protein matches found +if strcmpi(action,'display') && ~isempty(conflicts{1}) + displayErrorMessage(conflicts,swissprot,kegg) +end + +end +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function database = standardizeDatabase(database) +for i = 1:length(database) + database{i,3} = strsplit(database{i,3},' '); +end +end +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function str = union_string(cell_array) +%Receives any 1xn cell array and returns the union of all non empty +%elements as a string +nonempty = ~cellfun(@isempty,cell_array); +str = strjoin(cell_array(nonempty)',' '); +end +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function [uni,EC,MW,genes] = addMultipleMatches(uni,EC,MW,genes,conflicts,DB) +for i=1:length(conflicts{1}) + indexes = conflicts{2}{i}; + for j=2:length(indexes) + indx = indexes(j); + uni = [uni; DB{indx,1}]; + ECset = getECstring('',DB{indx,4}); + EC = [EC; {ECset}]; + MW = [MW; DB{indx,5}]; + genes = [genes; conflicts{1}{i}]; + end +end +end +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function displayErrorMessage(conflicts,swissprot,kegg) +STR = '\n Some genes with multiple associated proteins were found, please'; +STR = [STR, ' revise case by case in the protDatabase.mat file:\n\n']; +for i=1:length(conflicts{1}) + if strcmpi(conflicts{4}{i},swissprot) + DB = swissprot; + else + DB = kegg; + end + proteins = DB(conflicts{3}{i},1); + STR = [STR, '- gene: ' conflicts{2}{i} ' Proteins: ' strjoin(proteins) '\n']; +end +STR = [STR, '\nIf a wrongly annotated case was found then call the ']; +STR = [STR, 'getEnzymeCodes.m function again with the option action']; +STR = [STR, '= ignore\n\n']; +STR = [STR, 'If the conflicting proteins are desired to be kept as isoenzymes']; +STR = [STR, ' then call the getEnzymeCodes.m function']; +STR = [STR, ' again with the option action = add\n']; +error(sprintf(STR)) +end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \ No newline at end of file From d9872ac0988c7b2c1f559f9c248971ae1baeff15 Mon Sep 17 00:00:00 2001 From: Eduard Kerkhoven Date: Tue, 23 Oct 2018 22:03:12 +0200 Subject: [PATCH 03/14] doc: findInDb specify parameters --- geckomat/get_enzyme_data/findInDB.m | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/geckomat/get_enzyme_data/findInDB.m b/geckomat/get_enzyme_data/findInDB.m index bb13f58c6..351e18403 100644 --- a/geckomat/get_enzyme_data/findInDB.m +++ b/geckomat/get_enzyme_data/findInDB.m @@ -3,9 +3,10 @@ % Gets the uniprots and EC numbers for a given rxn into a given database % % grRule Genes association for a given metabolic reaction -% DB Cell array containing genes-> protein relationships, EC#s,MWs -% and sequences coming from external databases (Swissprot or -% KEGG) +% DBprot array of uniprot IDs, taken from swissprot or kegg database +% DBgenes array of genes corresponding to DBprot +% DBecNum array of EC numbers corresponding to DBprot +% DBMW array of molecular weights in Da corresponding to DBprot % % uni Array containing all the uniprot IDs related to an isoenzyme, % for a given reaction in each of its cells. @@ -15,7 +16,7 @@ % for a given reaction in each of its cells. % conflicts Contains those genes with multiple protein matches. % -% Usage: [uni,EC,MW,Genes,conflicts] = findInDB(grRule,DB,action) +% Usage: [uni,EC,MW,Genes,conflicts] = findInDB(grRule,DBprot,DBgenes,DBecNum,DBMW) % % Benjamin J. Sanchez, 2017-08-10 % Ivan Domenzain, 2018-09-06 From a0146ee12fee7579e7fe675d8187ca3006181687 Mon Sep 17 00:00:00 2001 From: benjasanchez Date: Wed, 24 Oct 2018 15:17:29 +0200 Subject: [PATCH 04/14] doc: flattenCell.m origin --- geckomat/get_enzyme_data/flattenCell.m | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/geckomat/get_enzyme_data/flattenCell.m b/geckomat/get_enzyme_data/flattenCell.m index 1429ae4fa..8c0ebe29c 100644 --- a/geckomat/get_enzyme_data/flattenCell.m +++ b/geckomat/get_enzyme_data/flattenCell.m @@ -8,10 +8,12 @@ % CFLAT = FLATTENCELL(C,STRFLAG) if STRFLAG is TRUE, empty entries in the % resulting CFLAT will be replaced with empty strings {''}. Default = FALSE % +% NOTE: This function is scheduled to be added to a new release of the +% RAVEN toolbox, i.e. can be removed from GECKO after that: +% https://github.com/SysBioChalmers/RAVEN/blob/d992169e9da44ed9c441753bcf77cc83f521e953/external/MetaNetX/flattenCell.m % % Jonathan Robinson, 2018-03-07 - if nargin < 2 strFlag = false; end @@ -38,6 +40,3 @@ if ( strFlag ) Cflat(cellfun(@isempty,Cflat)) = {''}; end - - - From e1f53d1b46b6f08549acd8b5ab79b9761541a635 Mon Sep 17 00:00:00 2001 From: benjasanchez Date: Thu, 25 Oct 2018 12:20:19 +0200 Subject: [PATCH 05/14] fix: remove .mat models + adapted saveECmodel.m to not keep saving them --- models/ecYeastGEM/ecYeastGEM.mat | Bin 656554 -> 0 bytes models/ecYeastGEM/ecYeastGEM_batch.mat | Bin 660153 -> 0 bytes models/saveECmodel.m | 8 ++------ 3 files changed, 2 insertions(+), 6 deletions(-) delete mode 100644 models/ecYeastGEM/ecYeastGEM.mat delete mode 100644 models/ecYeastGEM/ecYeastGEM_batch.mat diff --git 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