fuzzyKcatMatching

 fuzzyKcatMatching
   Matchs the model EC numbers and substrates to the BRENDA database, to
   return the corresponding kcats for each reaction. If no exact match is
   found, less specific kcat values are found from (a) evolutionary
   closely related organism; (b) different substrate; (c) calculated from
   specific activities; (d) wildcards in the EC number. The model organism
   is provided in the model adapter as obj.params.org_name, and
   evolutionary distance to other organisms is determined via KEGG
   phylogeny. If an organism name occurs multiple times in KEGG, the first
   instance will be used when determining evolutionary distance.

 Input:
   model        an ecModel in GECKO 3 format (with ecModel.ec structure)
   ecRxns       for which reactions (from model.ec.rxns) kcat values should
                be found, provided as logical vector with same length as
                model.ec.rxns. (Opt, default is all reactions)
   modelAdapter a loaded model adapter (Optional, will otherwise use the
                default model adapter).
   forceWClvl   force a minimum wildcard level (Optional, default 0). 

 Output:
   kcatList    structure array with list of BRENDA derived kcat values,
               with separate entries for each kcat value
               source      'brenda'           
               rxns        reaction identifiers
               substrate   substrate names
               kcat        proposed kcat value in /sec
               eccodes     as used to query BRENDA
               wildCardLvl which level of EC wild-card was necessary to
                           find a match
                           0: w.x.y.z
                           1: w.x.y.-
                           2: w.x.-.-
                           3: w.-.-.-
               origin      which level of specificity was necessary to
                           find a match
                           1: correct organism, correct substrate, kcat
                           2: any organism, correct substrate, kcat
                           3: correct organism, any substrate, kcat
                           4: any organism, any substrate, kcat
                           5: correct organism, specific activity
                           6: any organism, specific activity

   Note: If a wildcard is used, origin levels 1 and 2 are ignored. The
   last digits in the E.C. number indicate the substrate specificity, so
   if this should be ignored, then correct substrate matches should not be
   prioritized.

 Usage:
   kcatList = fuzzyKcatMatching(model, ecRxns, modelAdapter, forceWClvl)

0001 function kcatList = fuzzyKcatMatching(model, ecRxns, modelAdapter, forceWClvl)
0002 % fuzzyKcatMatching
0003 %   Matchs the model EC numbers and substrates to the BRENDA database, to
0004 %   return the corresponding kcats for each reaction. If no exact match is
0005 %   found, less specific kcat values are found from (a) evolutionary
0006 %   closely related organism; (b) different substrate; (c) calculated from
0007 %   specific activities; (d) wildcards in the EC number. The model organism
0008 %   is provided in the model adapter as obj.params.org_name, and
0009 %   evolutionary distance to other organisms is determined via KEGG
0010 %   phylogeny. If an organism name occurs multiple times in KEGG, the first
0011 %   instance will be used when determining evolutionary distance.
0012 %
0013 % Input:
0014 %   model        an ecModel in GECKO 3 format (with ecModel.ec structure)
0015 %   ecRxns       for which reactions (from model.ec.rxns) kcat values should
0016 %                be found, provided as logical vector with same length as
0017 %                model.ec.rxns. (Opt, default is all reactions)
0018 %   modelAdapter a loaded model adapter (Optional, will otherwise use the
0019 %                default model adapter).
0020 %   forceWClvl   force a minimum wildcard level (Optional, default 0).
0021 %
0022 % Output:
0023 %   kcatList    structure array with list of BRENDA derived kcat values,
0024 %               with separate entries for each kcat value
0025 %               source      'brenda'
0026 %               rxns        reaction identifiers
0027 %               substrate   substrate names
0028 %               kcat        proposed kcat value in /sec
0029 %               eccodes     as used to query BRENDA
0030 %               wildCardLvl which level of EC wild-card was necessary to
0031 %                           find a match
0032 %                           0: w.x.y.z
0033 %                           1: w.x.y.-
0034 %                           2: w.x.-.-
0035 %                           3: w.-.-.-
0036 %               origin      which level of specificity was necessary to
0037 %                           find a match
0038 %                           1: correct organism, correct substrate, kcat
0039 %                           2: any organism, correct substrate, kcat
0040 %                           3: correct organism, any substrate, kcat
0041 %                           4: any organism, any substrate, kcat
0042 %                           5: correct organism, specific activity
0043 %                           6: any organism, specific activity
0044 %
0045 %   Note: If a wildcard is used, origin levels 1 and 2 are ignored. The
0046 %   last digits in the E.C. number indicate the substrate specificity, so
0047 %   if this should be ignored, then correct substrate matches should not be
0048 %   prioritized.
0049 %
0050 % Usage:
0051 %   kcatList = fuzzyKcatMatching(model, ecRxns, modelAdapter, forceWClvl)
0052 
0053 if nargin<2 || isempty(ecRxns)
0054     ecRxns = true(numel(model.ec.rxns),1);
0055 elseif isnumeric(ecRxns)
0056     ecRxnsVec = false(numel(model.ec.rxns),1);
0057     ecRxnsVec(ecRxns) = true;
0058     ecRxns = ecRxnsVec;
0059 end
0060 ecRxns=find(ecRxns); % Get indices instead of logical
0061 
0062 if nargin < 3 || isempty(modelAdapter)
0063     modelAdapter = ModelAdapterManager.getDefault();
0064     if isempty(modelAdapter)
0065         error('Either send in a modelAdapter or set the default model adapter in the ModelAdapterManager.')
0066     end
0067 end
0068 params = modelAdapter.params;
0069 
0070 if nargin < 4 || isempty(forceWClvl)
0071     forceWClvl = 0;
0072 end
0073 
0074 if ~isfield(model.ec,'eccodes')
0075     error('No EC codes defined in model.ec.eccodes. First run getECfromGEM() and/or getECfromDatabase().')
0076 end
0077 eccodes      = model.ec.eccodes(ecRxns);
0078 substrates   = cell(numel(ecRxns),1);
0079 substrCoeffs = cell(numel(ecRxns),1);
0080 
0081 %Need to remove the prefix of GECKO light rxn names in the ec structure
0082 if ~model.ec.geckoLight
0083     rxnNames = model.ec.rxns;
0084 else
0085     rxnNames = extractAfter(model.ec.rxns, 4);
0086 end
0087 [~,originalRxns] = ismember(rxnNames(ecRxns),model.rxns);
0088 for i = 1:length(ecRxns)
0089     sel = find(model.S(:,originalRxns(i)) < 0);
0090     substrates{i}  = model.metNames(sel); 
0091     substrCoeffs{i} = -model.S(sel,originalRxns(i));
0092 end
0093 
0094 %Load BRENDA data:
0095 [KCATcell, SAcell] = loadBRENDAdata(modelAdapter);
0096 
0097 %Creates a Structure with KEGG codes for organisms, names and taxonomical
0098 %distance matrix and extract the organism index in the KEGG struct
0099 phylDistStruct =  KEGG_struct(modelAdapter.getPhylDistStructPath());
0100 %Get the KEGG code for the model's organism
0101 org_name       = params.org_name;
0102 org_index      = find_inKEGG(org_name,phylDistStruct.names);
0103 %build an index for genus in the phyl dist struct
0104 %first just extract the genus (i.e. the first part of the name)
0105 phylDistStruct.genus = lower(regexprep(phylDistStruct.names,'\s.*',''));
0106 %create a map for the genuses
0107 phylDistStruct.uniqueGenusList = unique(phylDistStruct.genus);
0108 phylDistStruct.genusHashMap = containers.Map(phylDistStruct.uniqueGenusList,1:length(phylDistStruct.uniqueGenusList));
0109 phylDistStruct.uniqueGenusIndices = cell(length(phylDistStruct.uniqueGenusList),1);
0110 
0111 %Then for each genus create a list with indices to the names
0112 for i = 1:length(phylDistStruct.genus)
0113     matchInd = cell2mat(values(phylDistStruct.genusHashMap,phylDistStruct.genus(i)));
0114     phylDistStruct.uniqueGenusIndices{matchInd} = [phylDistStruct.uniqueGenusIndices{matchInd};i];
0115 end
0116 
0117 %Allocate output
0118 kcats = zeros(length(eccodes),1);
0119 mM = length(eccodes);
0120 
0121 %Create empty kcatInfo
0122 %Legacy, no longer given as output, rather used to construct
0123 %kcatList.wildcardLvl and kcatList.origin.
0124 kcatInfo.info.org_s   = zeros(mM,1);
0125 kcatInfo.info.rest_s  = zeros(mM,1);
0126 kcatInfo.info.org_ns  = zeros(mM,1);
0127 kcatInfo.info.rest_ns = zeros(mM,1);
0128 kcatInfo.info.org_sa  = zeros(mM,1);
0129 kcatInfo.info.rest_sa = zeros(mM,1);
0130 kcatInfo.info.wcLevel = NaN(mM,1);
0131 kcatInfo.stats.queries  = 0;
0132 kcatInfo.stats.org_s    = 0;
0133 kcatInfo.stats.rest_s   = 0;
0134 kcatInfo.stats.org_ns   = 0;
0135 kcatInfo.stats.rest_ns  = 0;
0136 kcatInfo.stats.org_sa   = 0;
0137 kcatInfo.stats.rest_sa  = 0;
0138 kcatInfo.stats.wc0      = 0;
0139 kcatInfo.stats.wc1      = 0;
0140 kcatInfo.stats.wc2      = 0;
0141 kcatInfo.stats.wc3      = 0;
0142 kcatInfo.stats.wc4      = 0;
0143 kcatInfo.stats.matrix   = zeros(6,5);
0144 
0145 %build an EC index to speed things up a bit - many of the ECs appear
0146 %many times - unnecessary to compare them all
0147 %so, here, each EC string appears only once, and you get a vector with
0148 %indices to the rows in KCATcell
0149 [ECIndexIds,~,ic] = unique(KCATcell{1});
0150 EcIndexIndices = cell(length(ECIndexIds),1);
0151 for i = 1:length(EcIndexIndices)
0152     EcIndexIndices{i} = find(ic == i).';
0153 end
0154 
0155 %Apply force wildcard level
0156 while forceWClvl > 0
0157     eccodes=regexprep(eccodes,'(.)*(\.\d+)(\.-)*$','$1\.-$3');
0158     forceWClvl = forceWClvl - 1;
0159 end
0160 if forceWClvl == 1
0161     eccodes = regexprep(eccodes,'.*','-\.-\.-\.-');
0162 end
0163 
0164 progressbar('Gathering kcat values by fuzzy matching to BRENDA database')
0165 %Main loop:
0166 for i = 1:mM
0167     %Match:
0168     EC = eccodes{i};
0169     if ~isempty(EC)
0170         EC = strsplit(EC,';');
0171         %Try to match direct reaction:
0172         if ~isempty(substrates{i})
0173             [kcats(i), kcatInfo.info,kcatInfo.stats] = iterativeMatch(EC,substrates{i},substrCoeffs{i},i,KCATcell,...
0174                 kcatInfo.info,kcatInfo.stats,org_name,...
0175                 phylDistStruct,org_index,SAcell,ECIndexIds,EcIndexIndices);
0176         end
0177     end
0178     progressbar(i/mM)
0179 end
0180 
0181 kcatList.source      = 'brenda';
0182 kcatList.rxns        = model.ec.rxns(ecRxns);
0183 kcatList.substrates  = substrates;
0184 kcatList.kcats       = kcats;
0185 kcatList.eccodes     = eccodes;
0186 kcatList.wildcardLvl = kcatInfo.info.wcLevel;
0187 kcatList.origin      = NaN(numel(model.ec.rxns(ecRxns)),1);
0188 % This can be refactored, iterativeMatch and their nested functions can
0189 % just directly report the origin number.
0190 origin = [kcatInfo.info.org_s kcatInfo.info.rest_s kcatInfo.info.org_ns kcatInfo.info.rest_ns kcatInfo.info.org_sa kcatInfo.info.rest_sa];
0191 for i=1:6
0192     kcatList.origin(find(origin(:,i))) = i;
0193 end
0194 end
0195 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0196 function [kcat,dir,tot] =iterativeMatch(EC,subs,substrCoeff,i,KCATcell,dir,tot,...
0197     name,phylDist,org_index,SAcell,ECIndexIds,EcIndexIndices)
0198 %Will iteratively try to match the EC number to some registry in BRENDA,
0199 %using each time one additional wildcard.
0200 
0201 kcat    = zeros(size(EC));
0202 origin  = zeros(size(EC));
0203 matches = zeros(size(EC));
0204 wc_num  = ones(size(EC)).*1000;
0205 for k = 1:length(EC)
0206     success  = false;
0207     while ~success
0208         %Atempt match:
0209         [kcat(k),origin(k),matches(k)] = mainMatch(EC{k},subs,substrCoeff,KCATcell,...
0210             name,phylDist,...
0211             org_index,SAcell,ECIndexIds,EcIndexIndices);
0212         %If any match found, ends. If not, introduces one extra wild card and
0213         %tries again:
0214         if origin(k) > 0
0215             success   = true;
0216             wc_num(k) = sum(EC{k}=='-');
0217         else
0218             dot_pos  = [2 strfind(EC{k},'.')];
0219             wild_num = sum(EC{k}=='-');
0220             wc_text  = '-.-.-.-';
0221             EC{k}    = [EC{k}(1:dot_pos(4-wild_num)) wc_text(1:2*wild_num+1)];
0222         end
0223     end
0224 end
0225 
0226 if sum(origin) > 0
0227     %For more than one EC: Choose the maximum value among the ones with the
0228     %less amount of wildcards and the better origin:
0229     best_pos   = (wc_num == min(wc_num));
0230     new_origin = origin(best_pos);
0231     best_pos   = (origin == min(new_origin(new_origin~=0)));
0232     max_pos    = find(kcat == max(kcat(best_pos)));
0233     wc_num     = wc_num(max_pos(1));
0234     origin     = origin(max_pos(1));
0235     matches    = matches(max_pos(1));
0236     kcat       = kcat(max_pos(1));
0237 
0238     %Update dir and tot:
0239     dir.org_s(i)   = matches*(origin == 1);
0240     dir.rest_s(i)  = matches*(origin == 2);
0241     dir.org_ns(i)  = matches*(origin == 3);
0242     dir.org_sa(i)  = matches*(origin == 4);
0243     dir.rest_ns(i) = matches*(origin == 5);
0244     dir.rest_sa(i) = matches*(origin == 6);
0245     dir.wcLevel(i) = wc_num;
0246     tot.org_s        = tot.org_s   + (origin == 1);
0247     tot.rest_s       = tot.rest_s  + (origin == 2);
0248     tot.org_ns       = tot.org_ns  + (origin == 3);
0249     tot.org_sa       = tot.org_sa  + (origin == 4);
0250     tot.rest_ns      = tot.rest_ns + (origin == 5);
0251     tot.rest_sa      = tot.rest_sa + (origin == 6);
0252     tot.wc0          = tot.wc0     + (wc_num == 0);
0253     tot.wc1          = tot.wc1     + (wc_num == 1);
0254     tot.wc2          = tot.wc2     + (wc_num == 2);
0255     tot.wc3          = tot.wc3     + (wc_num == 3);
0256     tot.wc4          = tot.wc4     + (wc_num == 4);
0257     tot.queries      = tot.queries + 1;
0258     tot.matrix(origin,wc_num+1) = tot.matrix(origin,wc_num+1) + 1;
0259 end
0260 
0261 end
0262 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0263 
0264 function [kcat,origin,matches] = mainMatch(EC,subs,substrCoeff,KCATcell,...
0265     name,phylDist,org_index,SAcell,ECIndexIds,EcIndexIndices)
0266 
0267 %First make the string matching. This takes time, so we only want to do
0268 %this once:
0269 %Relaxes matching if wild cards are present:
0270 wild     = false;
0271 wild_pos = strfind(EC,'-');
0272 if ~isempty(wild_pos)
0273     EC   = EC(1:wild_pos(1)-1);
0274     wild = true;
0275 end
0276 stringMatchesEC_cell = extract_string_matches(EC,KCATcell{1},wild,ECIndexIds,EcIndexIndices);
0277 
0278 % Matching function prioritizing organism and substrate specificity when
0279 % available.
0280 
0281 origin = 0;
0282 %First try to match organism and substrate:
0283 [kcat,matches] = matchKcat(EC,subs,substrCoeff,KCATcell,name,true,false,...
0284     phylDist,org_index,SAcell,stringMatchesEC_cell,[]);
0285 if matches > 0 && ~wild % If wildcard, ignore substrate match
0286     origin = 1;
0287     %If no match, try the closest organism but match the substrate:
0288 else
0289     [kcat,matches] = matchKcat(EC,subs,substrCoeff,KCATcell,'',true,false,...
0290         phylDist,org_index,SAcell,stringMatchesEC_cell,[]);
0291     if matches > 0 && ~wild % If wildcard, ignore substrate match
0292         origin = 2;
0293         %If no match, try to match organism but with any substrate:
0294     else
0295         [kcat,matches] = matchKcat(EC,subs,substrCoeff,KCATcell,name,false,false,...
0296             phylDist,org_index,SAcell,stringMatchesEC_cell,[]);
0297         if matches > 0
0298             origin = 3;
0299             %If no match, try to match organism but for any substrate (SA*MW):
0300         else
0301             %create matching index for SA, has not been needed until now
0302             stringMatchesSA = extract_string_matches(EC,SAcell{1},wild,[],[]);
0303 
0304             [kcat,matches] = matchKcat(EC,subs,substrCoeff,KCATcell,name,false,...
0305                 true,phylDist,org_index,...
0306                 SAcell,stringMatchesEC_cell,stringMatchesSA);
0307             if matches > 0
0308                 origin = 4;
0309                 %If no match, try any organism and any substrate:
0310             else
0311                 [kcat,matches] = matchKcat(EC,subs,substrCoeff,KCATcell,'',false,...
0312                     false,phylDist,...
0313                     org_index,SAcell,stringMatchesEC_cell,stringMatchesSA);
0314                 if matches > 0
0315                     origin = 5;
0316                     %Again if no match, look for any org and SA*MW
0317                 else
0318                     [kcat,matches] = matchKcat(EC,subs,substrCoeff,KCATcell,'',...
0319                         false,true,phylDist,...
0320                         org_index,SAcell,stringMatchesEC_cell,stringMatchesSA);
0321                     if matches > 0
0322                         origin = 6;
0323                     end
0324                 end
0325 
0326             end
0327         end
0328     end
0329 end
0330 end
0331 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0332 function [kcat,matches] = matchKcat(EC,subs,substrCoeff,KCATcell,organism,...
0333     substrate,SA,phylDist,...
0334     org_index,SAcell,KCATcellMatches,SAcellMatches)
0335 
0336 %Will go through BRENDA and will record any match. Afterwards, it will
0337 %return the average value and the number of matches attained.
0338 kcat    = [];
0339 matches = 0;
0340 
0341 if SA
0342     %SAcell{1},wild,[],[]
0343     EC_indexes = extract_indexes(SAcellMatches,[],SAcell{2},subs,substrate,...
0344         organism,org_index,phylDist);
0345 
0346     kcat       = SAcell{3}(EC_indexes);
0347     org_cell   = SAcell{2}(EC_indexes);
0348     MW_BRENDA  = SAcell{4}(EC_indexes);
0349 
0350 else
0351     %KCATcell{1},wild,ECIndexIds,EcIndexIndices
0352     EC_indexes = extract_indexes(KCATcellMatches,KCATcell{2},KCATcell{3},...
0353         subs,substrate,organism,org_index,...
0354         phylDist);
0355     if substrate
0356         for j = 1:length(EC_indexes)
0357             indx = EC_indexes(j);
0358             for k = 1:length(subs)
0359                 if (isempty(subs{k}))
0360                     break;
0361                 end
0362                 %l = logical(strcmpi(model.metNames,subs{k}).*(model.S(:,i)~=0)); %I don't understand the .* (model.S(:,i)~=0) part, it shouldn't be needed?/JG;
0363                 if ~isempty(subs{k}) && strcmpi(subs{k},KCATcell{2}(indx))
0364                     if KCATcell{4}(indx) > 0
0365                         coeff = min(substrCoeff);
0366                         kCatTmp = KCATcell{4}(indx);
0367                         kcat  = [kcat;kCatTmp/coeff];
0368                     end
0369                 end
0370             end
0371         end
0372     else
0373         kcat = KCATcell{4}(EC_indexes);
0374     end
0375 end
0376 %Return maximum value:
0377 if isempty(kcat)
0378     kcat = 0;
0379 else
0380     matches        = length(kcat);
0381     [kcat,MaxIndx] = max(kcat);
0382 end
0383 %Avoid SA*Mw values over the diffusion limit rate  [Bar-Even et al. 2011]
0384 if kcat>(1E7)
0385     kcat = 1E7;
0386 end
0387 end
0388 
0389 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0390 %Make the string matches of the ECs. This is heavy, so only do it once!
0391 %
0392 function EC_indexes = extract_string_matches(EC,EC_cell,wild,ECIndexIds,EcIndexIndices)
0393 EC_indexes = [];
0394 EC_indexesOld = [];
0395 if wild
0396     if (~isempty(ECIndexIds)) %In some cases the EC_cell is not from KCatCell
0397         X = find(contains(ECIndexIds, EC));
0398         for j = 1:length(X)
0399             EC_indexes = [EC_indexes,EcIndexIndices{X(j)}];
0400         end
0401     else %Not optimized
0402         for j=1:length(EC_cell)
0403             if strfind(EC_cell{j},EC)==1
0404                 EC_indexes = [EC_indexes,j];
0405             end
0406         end
0407     end
0408 else
0409     if (~isempty(ECIndexIds)) %In some cases the EC_cell is not from KCatCell
0410         mtch = find(strcmpi(EC,ECIndexIds));
0411         if (~isempty(mtch))
0412             EC_indexes = EcIndexIndices{mtch};
0413         end
0414     else %%Not optimized
0415         if ~isempty(EC_cell)
0416             EC_indexes = transpose(find(strcmpi(EC,EC_cell)));
0417         end
0418     end
0419 end
0420 
0421 end
0422 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0423 %Extract the indexes of the entries in the BRENDA data that meet the
0424 %conditions specified by the search criteria
0425 function EC_indexes = extract_indexes(EC_indCellStringMatches,subs_cell,orgs_cell,subs,...
0426     substrate,organism, org_index,...
0427     phylDist)
0428 
0429 EC_indexes = EC_indCellStringMatches;%reuse so the string comparisons are not run many times
0430 
0431 %If substrate=true then it will extract only the substrates appereances
0432 %indexes in the EC subset from the BRENDA cell array
0433 
0434 if substrate
0435     if (~isempty(EC_indexes)) %optimization
0436         Subs_indexes = [];
0437         for l = 1:length(subs)
0438             if (isempty(subs{l}))
0439                 break;
0440             end
0441             Subs_indexes = horzcat(Subs_indexes,EC_indexes(strcmpi(subs(l),...
0442                 subs_cell(EC_indexes))));
0443         end
0444         EC_indexes = Subs_indexes;
0445     end
0446 end
0447 
0448 EC_orgs = orgs_cell(EC_indexes);
0449 %If specific organism values are requested looks for all the organism
0450 %repetitions on the subset BRENDA cell array(EC_indexes)
0451 if string(organism) ~= ''
0452     EC_indexes = EC_indexes(strcmpi(string(organism),EC_orgs));
0453 
0454     %If KEGG code was assigned to the organism (model) then it will look for
0455     %the Kcat value for the closest organism
0456 elseif org_index~='*' %&& org_index~=''
0457     KEGG_indexes = [];temp = [];
0458 
0459     %For relating a phyl dist between the modelled organism and the organisms
0460     %on the BRENDA cell array it should search for a KEGG code for each of
0461     %these
0462     for j=1:length(EC_indexes)
0463         %Assigns a KEGG index for those found on the KEGG struct
0464         orgs_index = find(strcmpi(orgs_cell(EC_indexes(j)),phylDist.names),1);
0465         if ~isempty(orgs_index)
0466             KEGG_indexes = [KEGG_indexes; orgs_index];
0467             temp         = [temp;EC_indexes(j)];
0468             %For values related to organisms without KEGG code, then it will
0469             %look for KEGG code for the first organism with the same genus
0470         else
0471             org = orgs_cell{EC_indexes(j)};
0472             orgGenus = lower(regexprep(org,'\s.*',''));
0473             if isKey(phylDist.genusHashMap,orgGenus) %annoyingly, this seems to be needed
0474                 matchInd = cell2mat(values(phylDist.genusHashMap,{orgGenus}));
0475                 matches = phylDist.uniqueGenusIndices{matchInd};
0476                 k = matches(1);
0477                 KEGG_indexes = [KEGG_indexes;k];
0478                 temp         = [temp;EC_indexes(j)];
0479             end
0480         end
0481     end
0482     %Update the EC_indexes cell array
0483     EC_indexes = temp;
0484     %Looks for the taxonomically closest organism and saves the index of
0485     %its appearences in the BRENDA cell
0486     if ~isempty(EC_indexes)
0487         distances = phylDist.distMat(org_index,KEGG_indexes);
0488         EC_indexes = EC_indexes(distances == min(distances));
0489     end
0490 end
0491 end
0492 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0493 function org_index = find_inKEGG(org_name,names)
0494 org_index      = find(strcmpi(org_name,names));
0495 if numel(org_index)>1
0496     org_index = org_index(1);
0497 elseif isempty(org_index)
0498     org_name    = regexprep(org_name,'\s.*','');
0499     org_index   = find(strcmpi(org_name,names));
0500     if numel(org_index)>1
0501         org_index = org_index(1);
0502     elseif isempty(org_index)
0503         org_index = '*';
0504     end
0505 end
0506 end
0507 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
0508 function phylDistStruct =  KEGG_struct(phylpath)
0509 load(phylpath)
0510 phylDistStruct.ids   = transpose(phylDistStruct.ids);
0511 phylDistStruct.names = transpose(phylDistStruct.names);
0512 phylDistStruct.names = regexprep(phylDistStruct.names,'\s*\(.*','');
0513 end