Biology subjects

Medvedeva, A.

Publications and source records attributed to Medvedeva, A..

7 recordsLinked to original sources

bioRxiv2025

Physical-Chemical Approach to Identify Local Structural Determinants of Molecular Mechanisms: Case Study of Antimalarial Drug Pyronaridine and Crystal-Growth Inhibition

Understanding how specific molecular substructures control chemical behavior is central to rational molecular design and the development of new materials. However, most current predictive models offer limited mechanistic resolution at the fragmental level. We present a novel physical-chemical computational framework, which is based on systematically perturbing a parent molecule, to quantify fragment-level contributions to both a specific mechanistic action and a broader functional outcome. To test our theoretical approach, we investigated local structural contributions to pyronaridine (PY), a clinically used antimalarial drug with a mechanistically distinctive mode of inhibition of hematin crystal growth via step-bunching. Chemically plausible PY molecular analogs have been computationally generated by selectively removing or substituting functional groups hypothesized to influence either step-bunching mechanisms or whole-parasite blood-stage activity. For each analog, we predicted the probability of four different crystal-growth inhibition mechanisms using a centroid-based similarity model based on a small dataset of experimentally verified crystal-growth inhibitors. The blood-stage antimalarial activity has also been estimated using the MAIP platform. A systematic comparison of molecular analogs revealed that step-bunching mechanisms depend primarily on two protonated pyrrolidines, with chlorobenzene as a strong secondary contributor. In contrast, antimalarial activity is more distributed, relying on coordinated interactions between aromatic-heteroatom scaffolds and an amine linker. The obtained results demonstrate that our approach can disentangle position-specific and cooperative fragmental effects, offering mechanistically interpretable guidance for the design of mechanism-optimized inhibitors. The framework might be broadly applicable across chemical and materials domains where linking local structure to specific mechanisms is essential.

biochemistry↗

bioRxiv2024

Physical-Chemical Features Selection Reveals That Differences in Dipeptide Compositions Correlate Most with Protein-Protein Interactions

The ability to accurately predict protein-protein interactions is critically important for our understanding of major cellular processes. However, current experimental and computational approaches for identifying them are technically very challenging and still have limited success. We propose a new computational method for predicting protein-protein interactions using only primary sequence information. It utilizes a concept of physical-chemical similarity to determine which interactions will most probably occur. In our approach, the physical-chemical features of protein are extracted using bioinformatics tools for different organisms, and then they are utilized in a machine-learning method to identify successful protein-protein interactions via correlation analysis. It is found that the most important property that correlates most with the protein-protein interactions for all studied organisms is dipeptide amino acid compositions. The analysis is specifically applied to the bacterial two-component system that includes histidine kinase and transcriptional response regulators. Our theoretical approach provides a simple and robust method for quantifying the important details of complex mechanisms of biological processes.

bioinformatics↗

bioRxiv2023

The olfactory-based neurofeedback of the EEG alpha rhythm

Neurofeedback (NFB) is a form of biofeedback that enables subjects to monitor and control their own brain activity. To communicate with the subject, modern NFB training methodologies utilize various signaling mechanisms, typically visual and/or auditory stimuli. Olfaction has not been explored yet as a way to deliver NFB. Here we developed an olfactory-based NFB system based on electroencephalographic (EEG) recordings in human participants. The system incorporates an EEG recording apparatus, a custom olfactory display for the automated delivery of Sniffin Sticks, and a Python application for the conversion of EEG rhythms into NFB signals and controlling behavioral tasks. We tested occipital alpha rhythm as the source of NFB. Fifteen healthy participants were randomly assigned to three groups: olfactory neurofeedback, auditory NFB, and mock-olfactory NFB. NFB training resulted in an increase of alpha power in the true NFB groups, but not in the mock NFB group where the alpha power decreased, probably because of fatigue and drowsiness. Based on these results, we conclude that olfactory NFB is feasible, and lay out a framework for its future development.

neuroscience↗

bioRxiv2023

Differences in Relevant Physicochemical Properties Correlate with Synergistic Activity of Antimicrobial Peptides

With the urgent need for new medical approaches due to increased bacterial resistance to antibiotics, antimicrobial peptides (AMPs) have been considered as potential treatments for infections. Experiments indicate that combinations of several types of AMPs might be more effective at inhibiting bacterial growth with reduced toxicity and a lower likelihood of inducing bacteria resistance. The molecular mechanisms of AMP-AMP synergistic antimicrobial activity, however, remain not well understood. Here, we present a theoretical approach that allows us to relate the physicochemical properties of AMPs and their antimicrobial cooperativity. A concept of physicochemical similarity is introduced, and it is found that less similar AMPs with respect to certain physicochemical properties lead to greater synergy because of their complementary antibacterial actions. The analysis of correlations between the similarity and the antimicrobial properties allows us to effectively separate synergistic from non-synergistic AMPs pairs. Our theoretical approach can be used for the rational design of more effective AMPs combinations for specific bacterial targets, for clarifying the mechanisms of bacterial elimination, and for a better understanding of cooperativity phenomena in biological systems. Author summaryIt is impossible to imagine modern medicine without antibiotics. But there is a growing problem of increased bacterial resistance to them. These considerations stimulated a search for novel methods to defend against infections. Antimicrobial peptides (AMPs) came out as powerful antibacterial agents. It was also found that combinations of AMPs are even more efficient than individual peptides. The mechanisms of such synergistic activities, however, are not understood. We developed a computational framework that allows us to connect the physicochemical properties of AMPs and their abilities to cooperatively eliminate infections. It is found that less similar peptides might exhibit synergy because of their complementary antibacterial properties. Our theoretical approach might lead to a better rational design of new antimicrobial drugs.

biophysics↗

bioRxiv2022

Predicting Antimicrobial Activity for Untested Peptide-Based Drugs Using Collaborative Filtering and Link Prediction

The increase of bacterial resistance to currently available antibiotics has underlined the urgent need to develop new antibiotic drugs. Antimicrobial peptides (AMPs), alone or in combination with other peptides and/or existing antibiotics, have emerged as promising candidates for this task. However, given that there are thousands of known AMPs and an even larger number can be synthesized, it is inefficient to comprehensively test all of them using standard wet lab experimental methods. These observations stimulated an application of machine-learning methods to identify promising AMPs. Currently, machine learning studies frequently combine very different bacteria without considering bacteria-specific features or interactions with AMPs. In addition, the sparsity of current AMP data sets of antimicrobial activity disqualifies the application of traditional machine-learning methods or renders the results unreliable. Here we present a new approach, featuring neighborhood-based collaborative filtering, to predict with high accuracy a given bacterias response to untested AMPs, AMP-AMP combinations, and AMP-antibiotic combinations based on similarities between bacterial responses. Furthermore, we also developed a complementary bacteria-specific link approach that can be used to visualize networks of AMP-antibiotic combinations, enabling us to suggest new combinations that are likely to be effective. Our theoretical analysis of AMP physico-chemical features suggests that there is an optimal similarity between two different AMPs that exhibit strong synergistic behavior. This principle, alongside with our specific results, can be applied to find or design effective AMP-AMP combinations that are target-specific. Author summaryIt is well known that combinations of different antimicrobial peptides (AMPs), in comparison to single AMP species, can lead to more efficient antimicrobial activity, but the large number of possible combinations requires the application of efficient machine-learning methods. We developed a new approach consisting of collaborative filtering, link prediction, and AMP feature analysis to predict previously-unknown, bacteria-specific activity of AMP combinations, suggest novel synergistic AMP-antibiotic combinations, and guide future design of effective AMP-AMP combinations.

biophysics↗

bioRxiv2020

Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning

We aimed to replicate a published effect of transcranial direct-current stimulation (tDCS)-induced recognition enhancement over the human ventrolateral prefrontal cortex [1] and analyse the data with machine learning. We investigated effects over an adjacent region, the dorsolateral PFC. We found weak or absent effects over the VLPFC and DLPFC. We conducted machine learning studies to examine the effects of semantic and phonetic features on memorization, which revealed no effect of VLPFC tDCS on the original dataset or the current data. The highest contributing factor to memory performance was individual differences in memory not explained by word features, tDCS group, or sample size, while semantic, phonetic, and orthographic word characteristics did not contribute significantly. To our knowledge, this is the first tDCS study to investigate cognitive effects with machine learning, and future studies may benefit from studying physiological as well as cognitive effects with data-driven approaches and computational models. Author summaryNon-Invasive Brain Stimulation techniques (in our case, transcranial direct current stimulation) are widely used among neuroscientists to map cognitive processes, for example, memory, decision making, emotional processing. In many cases, the sample size is limited, or methods applied for the data analysis are questionable. Here we propose a replication study aiming at confirmation of memory enhancement as a result of the application of anodal transcranial direct stimulation over the ventrolateral prefrontal cortex. Additionally, the involvement of the dorsolateral prefrontal cortex into episodic memory was analyzed. We used a larger sample size and applied different data analysis methods, including machine learning techniques. Surprisingly, we did not replicate the results of the original study, and we found weak effects of memory impairment after stimulating the DLPFC. However, what is most remarkable is that we have observed no significant effect of tDCS involvement on memory performance. Machine learning methods revealed no effect of linguistic factors on tDCS effect for both the original study and the replication at the level of individual trials and participants. Our findings highlight the importance of considering individual differences and data on the level of a single trial; in our data, participants memory responses resembled guessing behavior when recognition performance was measured by AUROC and highlight the need for modifications in the memory test and the use of other performance measures.

neuroscience↗

bioRxiv2019

Offset-related brain activity in the ventrolateral prefrontal cortex promotes long-term memory formation of verbal events

Recent evidence suggests that brain activity following the offset of a stimulus during encoding contributes to long-term memory formation, however the exact mechanisms underlying offset-related encoding are still unclear. Here we used repetitive transcranial magnetic stimulation (rTMS) to investigate offset-related activity in the left ventrolateral prefrontal cortex (VLPFC). rTMS was administered at different points in time around stimulus offset while male and female participants encoded visually-presented words (first rTMS experiment) or pairs of words (second rTMS experiment) and the analyses focused on the effects of the stimulation on subsequent memory performance. The results show that rTMS administered at the offset of the stimuli, but not during online encoding, disrupted subsequent memory performance. In the first experiment we show that rTMS specifically disrupted encoding mechanisms initiated by the offset of the stimuli rather than general, post-stimulus processes. In the second experiment, we show a robust decline in associative memory performance when rTMS was delivered at the offset of the word pairs, suggesting that offset-related encoding may contribute to the binding of information into an episodic memory trace. A meta-analysis conducted on the two studies and on a previously published dataset confirmed that the involvement of the left VLPFC in memory formation is initiated by the offset of the stimulus. The offset of the stimulus may represent an event boundary that promotes the reinstatement of the previously experienced event and episodic binding.\n\nSIGNIFICANCE STATEMENTHow well an event is encoded predicts how well it is remembered, and verbal encoding is an important part of everyday memory that, if disrupted, can lead to difficulties and disorders. The timing of encoding processes relative to the presentation of an event is important for successful retrieval, and little is known about the interval immediately after an events presentation (post-stimulus offset) which is thought to involve critical encoding processes in the VLPFC and hippocampus. The current studies demonstrate that indeed, verbal encoding processes in the VLPFC that are necessary for memory formation are triggered by the offset of the word, and these processes may involve VLPFC-hippocampal interactions that promote binding of event features into a single, coherent memory trace.

neuroscience↗