Biology subjects

Klauda, J. B.

Publications and source records attributed to Klauda, J. B..

10 recordsLinked to original sources

bioRxiv2026

XAMPGen: An Interpretable SI Pipeline For Novel Antimicrobial Peptide Discovery

Antimicrobial peptides (AMPs) are short peptides with a broad spectrum of activity against bacterial pathogens, representing a promising avenue for combating the growing threat of antibiotic resistance. This study focuses on two main components: 1) generating novel AMPs with desired properties using generative SI (GenSI) and 2) utilizing explainable SI (XSI) methods to validate the GenSI model and derive biological insights. In this work, we fine-tuned ProGen, a decoder-only protein language model, on known AMPs to generate novel peptide sequences with strong antimicrobial properties. To advance the interpretability and reliability of the generative model, we applied Explainable SI (XSI) techniques. Specifically, we used a probing classifier and a regressor to identify cornerstone layers that encode functionally critical information. Self-attention analysis within these layers revealed residue level interdependencies and patterns underlying antimicrobial functions, which we validated against well-characterized AMPs from the literature. Overall, these methods provide mechanistic insight into the model's decision-making process, offering a principled basis for trusting the sequences it generates rather than treating the model as a black box. Candidate sequences were filtered based on predicted Minimum Inhibitory Concentration (MIC) along with human insights into the structural activities of the AMPs in bacterial membranes, and a final set of peptides was selected for experimental testing. Biological assays for determining MIC confirmed antimicrobial activity in all generated and tested peptides, with fluorescence and circular dichroism measurements providing structural insight. With our XSI approach, we are able to reveal correlations between amino acid sequence patterns and binding properties of these sequences to bacterial membrane mimics, that directly correlate with biological activities in cell-based assays. This builds confidence in that the learned amino acid interdependencies as applied to AMPs carries physico-chemical meaning and is relevant for discoveries of novel AMPs with therapeutic potential. This work demonstrates that interpretable generative language models can serve as effective and transparent tools for rational AMP discovery.

bioinformatics↗

bioRxiv2026

Species- and Topic-aware Representation Learning for Antimicrobial Peptide Discovery

Antimicrobial resistance poses a major global health challenge, necessitating efficient strategies to discover potent antimicrobial peptides (AMPs). While recent generative models can produce many candidate sequences, experimentally validating all generated peptides in wet labs is impractical due to the high costs and time involved in such measurements. As a result, there is a strong demand for accurate predictions of peptide efficacy, typically measured as the minimum inhibitory concentration (MIC). We introduce STAMP, a framework for Species- and Topic-aware Representation Learning in AMP Discovery. This unified machine learning framework allows for cross-species predictions of AMP activity. STAMP integrates protein language model embeddings with species conditioning and topic-aware representations that capture sequence-level patterns, enabling generalizable predictions across multiple bacterial species within a single model. We evaluated STAMP on three benchmark datasets, which include two previously published datasets and a newly curated dataset derived from DBAASP, addressing duplicates and inconsistencies systematically. STAMP achieved strong predictive performance across these datasets, demonstrating a Pearson correlation coefficient (PCC) of 0.837 and an R2 of 0.70, outperforming several baseline models. Importantly, we further validated our prediction model using peptides that were experimentally tested for their antimicrobial activity against E.coli. and S.epidermidis bacteria, demonstrating its real-world applicability. Furthermore, residue-level importance analyses provide insights into the sequence determinants governing antimicrobial activity. Together, these results establish STAMP as a scalable framework for MIC prediction and an effective computational tool for accelerating AMP discovery and optimization.

bioinformatics↗

bioRxiv2025

Membrane partition and structural reorganization induced by anti-psychotics with distinct clinical profiles

Antipsychotics (APs) are used in the treatment of severe mental disorders. Their mechanism of action involves interaction with multiple brain targets, notably the dopamine D2 receptors (D2R), where they compete with dopamine. Due to their lipophilic nature, APs also partition and accumulate in lipid membranes, particularly around the D2R and in synaptic vesicles. When intercalated into brain membranes, APs slowly accumulate and act as a reservoir, allowing their rapid release on demand to modulate neurotransmitter signaling. They also modify the physicochemical and mechanical properties of the lipid bilayer. These modifications can subsequently affect the conformational changes of embedded membrane proteins like the D2R. The present study investigated two major APs with different pharmacological and clinical profiles: chlorpromazine, which exerts its clinical activity mainly through a strong antagonistic action at the D2R, and clozapine, the weakest D2R antagonist of all APs. Surprisingly, although D2R antagonism is usually associated with AP potency, clozapine has repeatedly demonstrated clinical superior efficacy to all APs and is therefore recommended for treatment-resistant schizophrenia. The current work aims to extend the classical AP receptor mediated paradigmatic mode of action to their potential and unique membrane remodeling properties by thoroughly comparing their partitioning and impact on the physicochemical properties of the lipid membrane. Lipid model membranes mimicking synaptic vesicles have been investigated using a combination of several biophysical methods. The study aims to determine how the partitioning of the two APs modifies membrane order, phase transition, thickness, elasticity, phase separation, membrane integrity and charge. Differences have been demonstrated between these two compounds, which may further differ both over time as they accumulate as well as depending on their pre- or post-synaptic location.

biophysics↗

bioRxiv2024

Physically Interpretable Performance Metrics for Clustering

Clustering is a type of machine learning (ML) technique which is used to group huge amounts of data based on their similarity into separate groups or clusters. Clustering is a very important task which is nowadays used to analyze the huge and diverse amount of data coming out of molecular dynamics (MD) simulations. Typically, the data from the MD simulations in terms of their various frames in the trajectory are clustered into different groups and a representative element from each group is studied separately. Now a very important question coming in this process is what is the quality of the clusters that are obtained. There are several performance metrics that are available in literature like Silhouette index and Davies-Bouldin Index that are often used to analyze the quality of clustering. However, most of these metrics focus on the overlap or the similarity of the clusters in the reduced dimension that is used for clustering and do not focus on the physically important properties or the parameters of the system. To address this issue, we have developed two physically interpretable scoring metrics that focus on the physical parameters of the system that we are analyzing. We have used and tested our algorithm on three different systems (1) Ising model, (2) peptide folding and unfolding of WT HP35, (3) a protein-ligand trajectory of an enzyme and substrate and (4) a protein-ligand dissociated trajectory. We show that the scoring metrics provide us clusters that match with our physical intuition about the systems.

bioinformatics↗

bioRxiv2024

Effect of Mutations on Smlt1473 Binding to Various Substrates Using Molecular Dynamics Simulations

Smlt1473 is a polysaccharide lyase from Stenotrophomonas maltophilia whose crystal structure was solved recently using X-ray crystallography. There was an effort to study the effect of mutations on the activity of Smlt1473 binding to various substrates like hyaluronic acid(HA), mannuronic acid(ManA), and alginate. In this study, we use molecular docking and molecular dynamics simulations to investigate the effect of binding of various substrates (HA and ManA) to Smlt1473 and two of its mutants H221F and R312L. We further studied the stability in the binding of Smlt1473 to its various substrates as well as the role of fluctuations. Machine-Learning based clustering algorithms were used to group the entire simulation trajectory into various stable states. The molecular interactions Smlt1473 to the substrates were calculated and the importance of specific residues were tested with observed activity assays due to residue mutations. Overall, we find that the R218 plays an important role in substrate binding and thus impacting the activity due to the H221F mutant and R/L312 itself plays an important role in the R312 mutation. In addition, we have also found three more residues K56, R107, and R164 important for substrate binding which we further proceed to confirm using wet lab mutagenesis studies.

biophysics↗

bioRxiv2024

Molecular Dynamics of Phosphatidylcholine Model Membranes of Splenic Lymphoma Cells

In eukaryotic cell membranes, phosphatidylcholine (PC) is one of the most prevalent phospholipids. Using the CHARMM36 lipid force field, we conducted molecular dynamics (MD) simulations on phosphatidylcholine (PC) only headgroup with varied fatty acid chains. Specifically, we investigated five PC components: 1,2-diauroyl-sn-glycero-3-phospocholine (DLPC), 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC), 1,2-dipalmitoyl-sn-glycero-3- phospcholine (DPPC), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), and 1- stearoyl-2-oleoylphosphatidylcholine (SOPC) in both pure and cancer model of PCs. We analyzed various characteristics such as lipid area, lateral compressibility, deuterium order parameter, bilayer thickness, radial distribution functions, and electron density. Our research revealed that PCs in the cancer model membrane are larger in surface area per lipid compared with pure PC membranes. This suggests that our PC model for cancer PCs may be more permeable and porous compared to pure PC membraness. In general, chain order parameter values were lower in cancer PCs compared to pure PCs. The electron density studies of cancer PCs revealed a decrease in bilayer thickness as temperature increases, indicating that cancer PCs experience thinning at higher temperatures. Overall, our results give insight into significant differences between the cellular makeup and functioning of pure PCs and cancer PCs at the molecular level.

biophysics↗

bioRxiv2024

Chlorpromazine inhibits EAG1 channels by altering the coupling between the PAS, CNBH and pore domains

EAG1 depolarization-activated potassium selective channels are important targets for treatment of cancer and neurological disorders. EAG1 channels are formed by a tetrameric subunit assembly with each subunit containing an N-terminal Per-Arnt-Sim (PAS) domain and C-terminal cyclic nucleotide-binding homology (CNBH) domain. The PAS and CNBH domains from adjacent subunits interact and form an intracellular tetrameric ring that regulates the EAG1 channel gating, including the movement of the voltage sensor domain (VSD) from closed to open states. Small molecule ligands can inhibit EAG1 channels by binding to their PAS domains. However, the allosteric pathways of this inhibition are not known. Here we show that chlorpromazine, a PAS domain small molecule binder, alters interactions between the PAS and CNBH domains and decreases the coupling between the intracellular tetrameric ring and the pore of the channel, while having little effect on the coupling between the PAS and VSD domains. In addition, chlorpromazine binding to the PAS domain did not alter Cole-Moore shift characteristic of EAG1 channels, further indicating that chlorpromazine has no effect on VSD movement from the deep closed to opened states. Our study provides a framework for understanding global pathways of EAG1 channel regulation by small molecule PAS domain binders.

biophysics↗

bioRxiv2022

Deep attention based variational autoencoder for antimicrobial peptide discovery

Antimicrobial peptides (AMPs) have been proposed as a potential solution against multiresistant pathogens. Designing novel AMPs requires exploration of a vast chemical space which makes it a challenging problem. Recently natural language processing and generative deep learning have shown great promise in exploring the vast chemical space and generating new chemicals with desired properties. In this study we leverage a variational attention mechanism in the generative variational autoencoder where attention vector is also modeled as a latent vector. Variational attention helps with the diversity and quality of the generated AMPs. The generated AMPs from this model are novel, have high statistical fidelity and have similar physicochemical properties such as charge, hydrophobicity and hydrophobic moment to the real to the real antimicrobial peptides.

bioinformatics↗

bioRxiv2020

Exploring dynamics and network analysis of spike glycoprotein of SARS-COV-2

The ongoing pandemic caused by coronavirus SARS-COV-2 continues to rage with devastating consequences on human health and global economy. The spike glycoprotein on the surface of coronavirus mediates its entry into host cells and is the target of all current antibody design efforts to neutralize the virus. The glycan shield of the spike helps the virus to evade the human immune response by providing a thick sugar-coated barrier against any antibody. To study the dynamic motion of glycans in the spike protein, we performed microsecond-long MD simulation in two different states that correspond to the receptor binding domain in open or closed conformations. Analysis of this microsecond-long simulation revealed a scissoring motion on the N-terminal domain of neighboring monomers in the spike trimer. Role of multiple glycans in shielding of spike protein in different regions were uncovered by a network analysis, where the high betweenness centrality of glycans at the apex revealed their importance and function in the glycan shield. Microdomains of glycans were identified featuring a high degree of intra-communication in these microdomains. An antibody overlap analysis revealed the glycan microdomains as well as individual glycans that inhibit access to the antibody epitopes on the spike protein. Overall, the results of this study provide detailed understanding of the spike glycan shield, which may be utilized for therapeutic efforts against this crisis.

biophysics↗

bioRxiv2020

Critical Sequence Hot-spots for Binding of nCOV-2019 to ACE2 as Evaluated by Molecular Simulations

The novel coronavirus (nCOV-2019) outbreak has put the world on edge, causing millions of cases and hundreds of thousands of deaths all around the world, as of June 2020, let alone the societal and economic impacts of the crisis. The spike protein of nCOV-2019 resides on the virions surface mediating coronavirus entry into host cells by binding its receptor binding domain (RBD) to the host cell surface receptor protein, angiotensin converter enzyme (ACE2). Our goal is to provide a detailed structural mechanism of how nCOV-2019 recognizes and establishes contacts with ACE2 and its difference with an earlier coronavirus SARS-COV in 2002 via extensive molecular dynamics (MD) simulations. Numerous mutations have been identified in the RBD of nCOV-2019 strains isolated from humans in different parts of the world. In this study, we investigated the effect of these mutations as well as other Ala-scanning mutations on the stability of RBD/ACE2 complex. It is found that most of the naturally-occurring mutations to the RBD either strengthen or have the same binding affinity to ACE2 as the wild-type nCOV-2019. This may have implications for high human-to-human transmission of coronavirus in regions where these mutations have been found as well as any vaccine design endeavors since these mutations could act as antibody escape mutants. Furthermore, in-silico Ala-scanning and long-timescale MD simulations, highlight the crucial role of the residues at the interface of RBD and ACE2 that may be used as potential pharmacophores for any drug development endeavors. From an evolutional perspective, this study also identifies how the virus has evolved from its predecessor SARS-COV and how it could further evolve to become more infectious.

biophysics↗