MARS: Computing Three-Dimensional Alignments for Multiple Ligands Using Pairwise Similarities

The three-dimensional (3D) superimposition of molecules of one biological target reflecting their relative bioactive orientation is key for several ligand-based drug design studies (e.g., QSAR studies, pharmacophore modeling). However, with the lack of sufficient ligand-protein complex structures, an experimental alignment is difficult or often impossible to obtain. Several computational 3D alignment tools have been developed by academic or commercial groups to address this challenge. Here, we present a new approach, MARS (<u>M</u>ultiple <u>A</u>lignments by <u>R</u>OCS-based <u>S</u>imilarity), that is based on the pairwise alignment of all molecules within the data set using the tool ROCS (<u>R</u>apid <u>O</u>verlay of <u>C</u>hemical <u>S</u>tructures). Each pairwise alignment is scored, and the results are captured in a score matrix. The ideal superimposition of the compounds in the set is then identified by the analysis of the score matrix building stepwise a superimposition of all molecules. The algorithm exploits similarities among all molecules in the data set to compute an optimal 3D alignment. This alignment tool presented here can be used for several applications, including pharmacophore model generation, 3D QSAR modeling, 3D clustering, identification of structural outliers, and addition of compounds to an already existing alignment. Case studies are shown, validating the 3D alignments for six different data sets

Sulfamide as Zinc Binding Motif in Small Molecule Inhibitors of Activated Thrombin Activatable Fibrinolysis Inhibitor (TAFIa)

Previously disclosed TAFIa inhibitors having a urea zinc-binding motif were used as the starting point for the development of a novel class of highly potent inhibitors having a sulfamide zinc-binding motif. High-resolution X-ray cocrystal structures were used to optimize the structures and reveal a highly unusual sulfamide configuration. A selected sulfamide was profiled in vitro and in vivo and displayed a promising ADMET profile

Sulfamide as Zinc Binding Motif in Small Molecule Inhibitors of Activated Thrombin Activatable Fibrinolysis Inhibitor (TAFIa)

Previously disclosed TAFIa inhibitors having a urea zinc-binding motif were used as the starting point for the development of a novel class of highly potent inhibitors having a sulfamide zinc-binding motif. High-resolution X-ray cocrystal structures were used to optimize the structures and reveal a highly unusual sulfamide configuration. A selected sulfamide was profiled in vitro and in vivo and displayed a promising ADMET profile

Sulfamide as Zinc Binding Motif in Small Molecule Inhibitors of Activated Thrombin Activatable Fibrinolysis Inhibitor (TAFIa)

Previously disclosed TAFIa inhibitors having a urea zinc-binding motif were used as the starting point for the development of a novel class of highly potent inhibitors having a sulfamide zinc-binding motif. High-resolution X-ray cocrystal structures were used to optimize the structures and reveal a highly unusual sulfamide configuration. A selected sulfamide was profiled in vitro and in vivo and displayed a promising ADMET profile

Novel Small Molecule Inhibitors of Activated Thrombin Activatable Fibrinolysis Inhibitor (TAFIa) from Natural Product Anabaenopeptin

Anabaenopeptins isolated from cyanobacteria were identified as inhibitors of carboxypeptidase TAFIa. Cocrystal structures of these macrocyclic natural product inhibitors in a modified porcine carboxypeptidase B revealed their binding mode and provided the basis for the rational design of small molecule inhibitors with a previously unknown central urea motif. Optimization based on these design concepts allowed for a rapid evaluation of the SAR and delivered potent small molecule inhibitors of TAFIa with a promising overall profile

Identification of High-Affinity P2Y₁₂ Antagonists Based on a Phenylpyrazole Glutamic Acid Piperazine Backbone

A series of novel, highly potent P2Y₁₂ antagonists as inhibitors of platelet aggregation based on a phenylpyrazole glutamic acid piperazine backbone is described. Exploration of the structural requirements of the substituents by probing the structure–activity relationship along this backbone led to the discovery of the <i>N</i>-acetyl-(<i>S</i>)-proline cyclobutyl amide moiety as a highly privileged motif. Combining the most favorable substituents led to remarkably potent P2Y₁₂ antagonists displaying not only low nanomolar binding affinity to the P2Y₁₂ receptor but also a low nanomolar inhibition of platelet aggregation in the human platelet rich plasma assay with IC₅₀ values below 50 nM. Using a homology and a three-dimensional quantitative structure–activity relationship model, a binding hypothesis elucidating the impact of several structural features was developed

Table4_AI/ML combined with next-generation sequencing of VHH immune repertoires enables the rapid identification of de novo humanized and sequence-optimized single domain antibodies: a prospective case study.xlsx

Introduction: In this study, we demonstrate the feasibility of yeast surface display (YSD) and nextgeneration sequencing (NGS) in combination with artificial intelligence and machine learning methods (AI/ML) for the identification of de novo humanized single domain antibodies (sdAbs) with favorable early developability profiles.Methods: The display library was derived from a novel approach, in which VHH-based CDR3 regions obtained from a llama (Lama glama), immunized against NKp46, were grafted onto a humanized VHH backbone library that was diversified in CDR1 and CDR2. Following NGS analysis of sequence pools from two rounds of fluorescence-activated cell sorting we focused on four sequence clusters based on NGS frequency and enrichment analysis as well as in silico developability assessment. For each cluster, long short-term memory (LSTM) based deep generative models were trained and used for the in silico sampling of new sequences. Sequences were subjected to sequence- and structure-based in silico developability assessment to select a set of less than 10 sequences per cluster for production.Results: As demonstrated by binding kinetics and early developability assessment, this procedure represents a general strategy for the rapid and efficient design of potent and automatically humanized sdAb hits from screening selections with favorable early developability profiles.</p

Table3_AI/ML combined with next-generation sequencing of VHH immune repertoires enables the rapid identification of de novo humanized and sequence-optimized single domain antibodies: a prospective case study.xlsx

Introduction: In this study, we demonstrate the feasibility of yeast surface display (YSD) and nextgeneration sequencing (NGS) in combination with artificial intelligence and machine learning methods (AI/ML) for the identification of de novo humanized single domain antibodies (sdAbs) with favorable early developability profiles.Methods: The display library was derived from a novel approach, in which VHH-based CDR3 regions obtained from a llama (Lama glama), immunized against NKp46, were grafted onto a humanized VHH backbone library that was diversified in CDR1 and CDR2. Following NGS analysis of sequence pools from two rounds of fluorescence-activated cell sorting we focused on four sequence clusters based on NGS frequency and enrichment analysis as well as in silico developability assessment. For each cluster, long short-term memory (LSTM) based deep generative models were trained and used for the in silico sampling of new sequences. Sequences were subjected to sequence- and structure-based in silico developability assessment to select a set of less than 10 sequences per cluster for production.Results: As demonstrated by binding kinetics and early developability assessment, this procedure represents a general strategy for the rapid and efficient design of potent and automatically humanized sdAb hits from screening selections with favorable early developability profiles.</p

Table6_AI/ML combined with next-generation sequencing of VHH immune repertoires enables the rapid identification of de novo humanized and sequence-optimized single domain antibodies: a prospective case study.xlsx

Introduction: In this study, we demonstrate the feasibility of yeast surface display (YSD) and nextgeneration sequencing (NGS) in combination with artificial intelligence and machine learning methods (AI/ML) for the identification of de novo humanized single domain antibodies (sdAbs) with favorable early developability profiles.Methods: The display library was derived from a novel approach, in which VHH-based CDR3 regions obtained from a llama (Lama glama), immunized against NKp46, were grafted onto a humanized VHH backbone library that was diversified in CDR1 and CDR2. Following NGS analysis of sequence pools from two rounds of fluorescence-activated cell sorting we focused on four sequence clusters based on NGS frequency and enrichment analysis as well as in silico developability assessment. For each cluster, long short-term memory (LSTM) based deep generative models were trained and used for the in silico sampling of new sequences. Sequences were subjected to sequence- and structure-based in silico developability assessment to select a set of less than 10 sequences per cluster for production.Results: As demonstrated by binding kinetics and early developability assessment, this procedure represents a general strategy for the rapid and efficient design of potent and automatically humanized sdAb hits from screening selections with favorable early developability profiles.</p

Table2_AI/ML combined with next-generation sequencing of VHH immune repertoires enables the rapid identification of de novo humanized and sequence-optimized single domain antibodies: a prospective case study.xlsx

Introduction: In this study, we demonstrate the feasibility of yeast surface display (YSD) and nextgeneration sequencing (NGS) in combination with artificial intelligence and machine learning methods (AI/ML) for the identification of de novo humanized single domain antibodies (sdAbs) with favorable early developability profiles.Methods: The display library was derived from a novel approach, in which VHH-based CDR3 regions obtained from a llama (Lama glama), immunized against NKp46, were grafted onto a humanized VHH backbone library that was diversified in CDR1 and CDR2. Following NGS analysis of sequence pools from two rounds of fluorescence-activated cell sorting we focused on four sequence clusters based on NGS frequency and enrichment analysis as well as in silico developability assessment. For each cluster, long short-term memory (LSTM) based deep generative models were trained and used for the in silico sampling of new sequences. Sequences were subjected to sequence- and structure-based in silico developability assessment to select a set of less than 10 sequences per cluster for production.Results: As demonstrated by binding kinetics and early developability assessment, this procedure represents a general strategy for the rapid and efficient design of potent and automatically humanized sdAb hits from screening selections with favorable early developability profiles.</p