Additional file 1 of PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity
Creators: name: Ammar, Ammar
name: Cavill, Rachel
name: Evelo, Chris
name: Willighagen, Egon
Publisher: name: figshare
Year: 2024
Subjects: subject: Biophysics
subject: Biochemistry
subject: Genetics
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Pharmacology
subject: Biotechnology
subject: Chemical Sciences not elsewhere classified
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Infectious Diseases
subject: FOS: Health sciences

description: Additional file 1: Table S1. Protein pairwise sequence similarity for PSnpBind proteins (26 in total). Table S2. HMMER search results against Pfam for PSnpBind proteins (26 in total). Table S3. Pairwise binding pocket similarity scores for the 26 proteins in descending ordered by score. The similarity was calculated from the fingerprints generated using FuzCav. The table contains only similar pockets with a similarity score &gt; 0.16 (as mentioned in Weill et al. [70]). Table S4. Optimal parameters for Random Forest models trained on six data splits using nested cross-validation. Data splits acronyms: BASR: Binding affinity-stratified random split, PS: Protein similarity-based split, BSS: Binding site similarity-based split, LS: Ligand similarity-based split, LWSR: Ligand weight-stratified random split, and LVSR: Ligand volume-stratified random split. Table S5. Optimal parameters for Decision Tree models trained on six data splits using nested cross-validation. Data splits acronyms: BASR: Binding affinity-stratified random split, PS: Protein similarity-based split, BSS: Binding site similarity-based split, LS: Ligand similarity-based split, LWSR: Ligand weight-stratified random split, and LVSR: Ligand volume-stratified random split. Table S6. Optimal parameters for Lasso Regression models trained on six data splits using nested cross-validation. Data splits acronyms: BASR: Binding affinity-stratified random split, PS: Protein similarity-based split, BSS: Binding site similarity-based split, LS: Ligand similarity-based split, LWSR: Ligand weight-stratified random split, and LVSR: Ligand volume-stratified split. Table S7. Optimal parameters for Ridge Regression models trained on six data splits using nested cross-validation. Data splits acronyms: BASR: Binding affinity-stratified random split, PS: Protein similarity-based split, BSS: Binding site similarity-based split, LS: Ligand similarity-based split, LWSR: Ligand weight-stratified random split, and LVSR: Ligand volume-stratified split. Figure S1. Diversity distribution of ligands in the random data split stratified on binding affinity. (A, B) Chemical space defined by PCA factorization; (C) chemical space defined by molecular weight as X-axis and XlogP as Y. Figure S2. Diversity distribution of ligands in the protein similarity-based data split. (A, B) Chemical space defined by PCA factorization; (C) chemical space defined by molecular weight as X-axis and XlogP as Y. Figure S3. Diversity distribution of ligands in the binding pocket similarity-based data split. (A, B) Chemical space defined by PCA factorization; (C) chemical space defined by molecular weight as X-axis and XlogP as Y. Figure S4. Diversity distribution of ligands in the ligand similarity-based data split. (A, B) Chemical space defined by PCA factorization; (C) chemical space defined by molecular weight as X-axis and XlogP as Y. Figure S5. Diversity distribution of ligands in the in the random data split stratified on ligand volume. (A, B) Chemical space defined by PCA factorization; (C) chemical space defined by molecular weight as X-axis and XlogP as Y.