Reconstructing the landscape of gut microbial species across 29,000 diverse individuals.

The human gut microbiome has been linked to health and disease. Investigation of the human microbiome has largely employed 16S amplicon sequencing, with limited ability to distinguish microbes at the species level. Herein, we describe the development of Reference-based Exact Mapping (RExMap) of microbial amplicon variants that enables mapping of microbial species from standard 16S sequencing data. RExMap analysis of 16S data captures ∼75% of microbial species identified by whole-genome shotgun sequencing, despite hundreds-fold less sequencing depth. RExMap re-analysis of existing 16S data from 29,349 individuals across 16 regions from around the world reveals a detailed landscape of gut microbial species across populations and geography. Moreover, RExMap identifies a core set of fifteen gut microbes shared by humans. Core microbes are established soon after birth and closely associate with BMI across multiple independent studies. RExMap and the human microbiome dataset are presented as resources with which to explore the role of the human microbiome.

Association of Physical Activity With Bioactive Lipids and Cardiovascular Events.

BACKGROUND: To clarify the mechanisms underlying physical activity (PA)-related cardioprotection, we examined the association of PA with plasma bioactive lipids (BALs) and cardiovascular disease (CVD) events. We additionally performed genome-wide associations. METHODS: PA-bioactive lipid associations were examined in VITAL (VITamin D and OmegA-3 TriaL)-clinical translational science center (REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT01169259; N=1032) and validated in JUPITER (Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin)-NC (REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT00239681; N=589), using linear models adjusted for age, sex, race, low-density lipoprotein-cholesterol, total-C, and smoking. Significant BALs were carried over to examine associations with incident CVD in 2 nested CVD case-control studies: VITAL-CVD (741 case-control pairs) and JUPITER-CVD (415 case-control pairs; validation). RESULTS: We detected 145 PA-bioactive lipid validated associations (false discovery rate <0.1). Annotations were found for 6 of these BALs: 12,13-diHOME, 9,10-diHOME, lysoPC(15:0), oxymorphone-3b-D-glucuronide, cortisone, and oleoyl-glycerol. Genetic analysis within JUPITER-NC showed associations of 32 PA-related BALs with 22 single-nucleotide polymorphisms. From PA-related BALs, 12 are associated with CVD. CONCLUSIONS: We identified a PA-related bioactive lipidome profile out of which 12 BALs also had opposite associations with incident CVD events.

Deepfake Histologic Images for Enhancing Digital Pathology.

A pathologist's optical microscopic examination of thinly cut, stained tissue on glass slides prepared from a formalin-fixed paraffin-embedded tissue blocks is the gold standard for tissue diagnostics. In addition, the diagnostic abilities and expertise of pathologists is dependent on their direct experience with common and rarer variant morphologies. Recently, deep learning approaches have been used to successfully show a high level of accuracy for such tasks. However, obtaining expert-level annotated images is an expensive and time-consuming task, and artificially synthesized histologic images can prove greatly beneficial. In this study, we present an approach to not only generate histologic images that reproduce the diagnostic morphologic features of common disease but also provide a user ability to generate new and rare morphologies. Our approach involves developing a generative adversarial network model that synthesizes pathology images constrained by class labels. We investigated the ability of this framework in synthesizing realistic prostate and colon tissue images and assessed the utility of these images in augmenting the diagnostic ability of machine learning methods and their usability by a panel of experienced anatomic pathologists. Synthetic data generated by our framework performed similar to real data when training a deep learning model for diagnosis. Pathologists were not able to distinguish between real and synthetic images, and their analyses showed a similar level of interobserver agreement for prostate cancer grading. We extended the approach to significantly more complex images from colon biopsies and showed that the morphology of the complex microenvironment in such tissues can be reproduced. Finally, we present the ability for a user to generate deepfake histologic images using a simple markup of sematic labels.

Influence of pretreatment exposure to pediatric dental care using the "Tiny dentist" game on 4-7 years old children's pain and anxiety: a parallel randomised clinical trial.

There have been no experiments on interactive modelling through computer games, although there have been a few on modelling a pre-exposure method for managing anxiety among preschoolers. The impact of partaking in a dental simulation game prior to the dental treatment on pain and anxiety in kids aged 4 to7 years during their first appointment was studied. A total of 156 kids who required unilateral pulp therapy and preformed crowns on their mandibular primary molars were enrolled in this double-blind, randomized clinical trial. They were then randomly assigned to intervention and control groups. The intervention group engaged in the game three times/day for 07 days prior to the anticipated appointment. The Wong-Baker Faces Rating Scale (WBFRS) was used to record their pre- and post-operative pain experienced during the dental procedure. Additionally, a finger pulse oximeter was used to record heart rate (HR) at each of the six treatment phases: baseline (the first session, two weeks prior to treatment) and stages 2-6. Playing video games considerably lowered the heart rate. Playing and the treatment period interacted in a major way. On comparing the groups at every time point, the intervention group displayed lower HR during injection, tooth preparation with an air-rotor and biomechanical preparation with endodontic rotary files.The results suggest that engaging in specific dental simulation games prior to the 1st dentist visit could help preschoolers feel less anxious during routine dental operations.

Deep Neural Networks for Classification of LC-MS Spectral Peaks.

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics has emerged as a valuable tool for biological discovery, capable of assaying thousands of diverse chemical entities in a single biospecimen. Processing of nontargeted LC-MS spectral data requires identification and isolation of true spectral features from the random, false noise peaks that comprise a significant portion of total signals, using inexact peak selection algorithms and time-consuming visual inspection of data. To increase the fidelity and speed of data processing, herein we establish, optimize, and evaluate a machine learning pipeline employing deep neural networks as well as a simpler multiple logistic regression model for classification of spectral features from nontargeted LC-MS metabolomics data. Machine learning-based approaches were found to remove up to 90% of false peaks from complex nontargeted LC-MS data sets without reducing true positive signals and exhibit excellent reproducibility across multiple data sets. Application of machine learning for nontargeted LC-MS-based peak selection provides for robust and scalable peak classification and data filtering, enabling handling and processing of large scale, complex metabolomics data sets.

PSIP1/p75 promotes tumorigenicity in breast cancer cells by promoting the transcription of cell cycle genes.

Breast cancer (BC) is a highly heterogeneous disease, both at the pathological and molecular level, and several chromatin-associated proteins play crucial roles in BC initiation and progression. Here, we demonstrate the role of PSIP1 (PC4 and SF2 interacting protein)/p75 (LEDGF) in BC progression. PSIP1/p75, previously identified as a chromatin-adaptor protein, is found to be upregulated in basal-like/triple negative breast cancer (TNBC) patient samples and cell lines. Immunohistochemistry in tissue arrays showed elevated levels of PSIP1 in metastatic invasive ductal carcinoma. Survival data analyses revealed that the levels of PSIP1 showed a negative association with TNBC patient survival. Depletion of PSIP1/p75 significantly reduced the tumorigenicity and metastatic properties of TNBC cell lines while its over-expression promoted tumorigenicity. Further, gene expression studies revealed that PSIP1 regulates the expression of genes controlling cell-cycle progression, cell migration and invasion. Finally, by interacting with RNA polymerase II, PSIP1/p75 facilitates the association of RNA pol II to the promoter of cell cycle genes and thereby regulates their transcription. Our findings demonstrate an important role of PSIP1/p75 in TNBC tumorigenicity by promoting the expression of genes that control the cell cycle and tumor metastasis.

Towards Translation of Discrete Frequency Infrared Spectroscopic Imaging for Digital Histopathology of Clinical Biopsy Samples.

Fourier transform infrared (FT-IR) spectroscopic imaging has been widely tested as a tool for stainless digital histology of biomedical specimens, including for the identification of infiltration and fibrosis in endomyocardial biopsy samples to assess transplant rejection. A major barrier in clinical translation has been the slow speed of imaging. To address this need, we tested and report here the viability of using high speed discrete frequency infrared (DFIR) imaging to obtain stain-free biochemical imaging in cardiovascular samples collected from patients. Images obtained by this method were classified with high accuracy by a Bayesian classification algorithm trained on FT-IR imaging data as well as on DFIR data. A single spectral feature correlated with instances of fibrosis, as identified by the pathologist, highlights the advantage of the DFIR imaging approach for rapid detection. The speed of digital pathologic recognition was at least 16 times faster than the fastest FT-IR imaging instrument. These results indicate that a fast, on-site identification of fibrosis using IR imaging has potential for real time assistance during surgeries. Further, the work describes development and applications of supervised classifiers on DFIR imaging data, comparing classifiers developed on FT-IR and DFIR imaging modalities and identifying specific spectral features for accurate identification of fibrosis. This addresses a topic of much debate on the use of training data and cross-modality validity of IR measurements. Together, the work is a step toward addressing a clinical diagnostic need at acquisition time scales that make IR imaging technology practical for medical use.

Defined Host-Guest Chemistry on Nanocarbon for Sustained Inhibition of Cancer.

Signal transducer and activator of transcription factor 3 (STAT-3) is known to be overexpressed in cancer stem cells. Poor solubility and variable drug absorption are linked to low bioavailability and decreased efficacy. Many of the drugs regulating STAT-3 expression lack aqueous solubility; hence hindering efficient bioavailability. A theranostics nanoplatform based on luminescent carbon particles decorated with cucurbit[6]uril is introduced for enhancing the solubility of niclosamide, a STAT-3 inhibitor. The host-guest chemistry between cucurbit[6]uril and niclosamide makes the delivery of the hydrophobic drug feasible while carbon nanoparticles enhance cellular internalization. Extensive physicochemical characterizations confirm successful synthesis. Subsequently, the host-guest chemistry of niclosamide and cucurbit[6]uril is studied experimentally and computationally. In vitro assessments in human breast cancer cells indicate approximately twofold enhancement in IC50 of drug. Fourier transform infrared and fluorescence imaging demonstrate efficient cellular internalization. Furthermore, the catalytic biodegradation of the nanoplatforms occur upon exposure to human myeloperoxidase in short time. In vivo studies on athymic mice with MCF-7 xenograft indicate the size of tumor in the treatment group is half of the controls after 40 d. Immunohistochemistry corroborates the downregulation of STAT-3 phosphorylation. Overall, the host-guest chemistry on nanocarbon acts as a novel arsenal for STAT-3 inhibition.

Tunable Luminescent Carbon Nanospheres with Well-Defined Nanoscale Chemistry for Synchronized Imaging and Therapy.

In this work, we demonstrate the significance of defined surface chemistry in synthesizing luminescent carbon nanomaterials (LCN) with the capability to perform dual functions (i.e., diagnostic imaging and therapy). The surface chemistry of LCN has been tailored to achieve two different varieties: one that has a thermoresponsive polymer and aids in the controlled delivery of drugs, and the other that has fluorescence emission both in the visible and near-infrared (NIR) region and can be explored for advanced diagnostic modes. Although these particles are synthesized using simple, yet scalable hydrothermal methods, they exhibit remarkable stability, photoluminescence and biocompatibility. The photoluminescence properties of these materials are tunable through careful choice of surface-passivating agents and can be exploited for both visible and NIR imaging. Here the synthetic strategy demonstrates the possibility to incorporate a potent antimetastatic agent for inhibiting melanomas in vitro. Since both particles are Raman active, their dispersion on skin surface is reported with Raman imaging and utilizing photoluminescence, their depth penetration is analysed using fluorescence 3D imaging. Our results indicate a new generation of tunable carbon-based probes for diagnosis, therapy or both.

Extracting knowledge from chemical imaging data using computational algorithms for digital cancer diagnosis.

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.

Colon Cancer Grading Using Infrared Spectroscopic Imaging-Based Deep Learning.

Tumor grade assessment is critical to the treatment of cancers. A pathologist typically evaluates grade by examining morphologic organization in tissue using hematoxylin and eosin (H&E) stained tissue sections. Fourier transform infrared spectroscopic (FT-IR) imaging provides an alternate view of tissue in which spatially specific molecular information from unstained tissue can be utilized. Here, we examine the potential of IR imaging for grading colon cancer in biopsy samples. We used a 148-patient cohort to develop a deep learning classifier to estimate the tumor grade using IR absorption. We demonstrate that FT-IR imaging can be a viable tool to determine colorectal cancer grades, which we validated on an independent cohort of surgical resections. This work demonstrates that harnessing molecular information from FT-IR imaging and coupling it with morphometry is a potential path to develop clinically relevant grade prediction models.

INFORM: INFrared-based ORganizational Measurements of tumor and its microenvironment to predict patient survival.

The structure and organization of a tumor and its microenvironment are often associated with cancer outcomes due to spatially varying molecular composition and signaling. A persistent challenge is to use this physical and chemical spatial organization to understand cancer progression. Here, we present a high-definition infrared imaging-based organizational measurement framework (INFORM) that leverages intrinsic chemical contrast of tissue to label unique components of the tumor and its microenvironment. Using objective and automated computational methods, further, we determine organization characteristics important for prediction. We show that the tumor spatial organization assessed with this framework is predictive of overall survival in colon cancer that adds to capability from clinical variables such as stage and grade, approximately doubling the risk of death in high-risk individuals. Our results open an all-digital avenue for measuring and studying the association between tumor spatial organization and disease progression.

Infrared Spectroscopic Imaging Visualizes a Prognostic Extracellular Matrix-Related Signature in Breast Cancer.

Molecular analysis techniques such as gene expression analysis and proteomics have contributed greatly to our understanding of cancer heterogeneity. In prior studies, gene expression analysis was shown to stratify patient outcome on the basis of tumor-microenvironment associated genes. A specific gene expression profile, referred to as ECM3 (Extracellular Matrix Cluster 3), indicated poorer survival in patients with grade III tumors. In this work, we aimed to visualize the downstream effects of this gene expression profile onto the tissue, thus providing a spatial context to altered gene expression profiles. Using infrared spectroscopic imaging, we identified spectral patterns specific to the ECM3 gene expression profile, achieving a high spectral classification performance of 0.87 as measured by the area under the curve of the receiver operating characteristic curve. On a patient level, we correctly identified 20 out of 22 ECM3 group patients and 19 out of 20 non-ECM3 group patients by using this spectroscopic imaging-based classifier. By comparing pixels that were identified as ECM3 or non-ECM3 with H&E and IHC images, we were also able to observe an association between tissue morphology and the gene expression clusters, showing the ability of our method to capture broad outcome associated features from infrared images.

Multi-functionality Redefined with Colloidal Carotene Carbon Nanoparticles for Synchronized Chemical Imaging, Enriched Cellular Uptake and Therapy.

Typically, multiplexing high nanoparticle uptake, imaging, and therapy requires careful integration of three different functions of a multiscale molecular-particle assembly. Here, we present a simpler approach to multiplexing by utilizing one component of the system for multiple functions. Specifically, we successfully synthesized and characterized colloidal carotene carbon nanoparticle (C(3)-NP), in which a single functional molecule served a threefold purpose. First, the presence of carotene moieties promoted the passage of the particle through the cell membrane and into the cells. Second, the ligand acted as a potent detrimental moiety for cancer cells and, finally, the ligands produced optical contrast for robust microscopic detection in complex cellular environments. In comparative tests, C(3)-NP were found to provide effective intracellular delivery that enables both robust detection at cellular and tissue level and presents significant therapeutic potential without altering the mechanism of intracellular action of ß-carotene. Surface coating of C(3) with phospholipid was used to generate C(3)-Lipocoat nanoparticles with further improved function and biocompatibility, paving the path to eventual in vivo studies.

Computational chemical imaging for cardiovascular pathology: chemical microscopic imaging accurately determines cardiac transplant rejection.

Rejection is a common problem after cardiac transplants leading to significant number of adverse events and deaths, particularly in the first year of transplantation. The gold standard to identify rejection is endomyocardial biopsy. This technique is complex, cumbersome and requires a lot of expertise in the correct interpretation of stained biopsy sections. Traditional histopathology cannot be used actively or quickly during cardiac interventions or surgery. Our objective was to develop a stain-less approach using an emerging technology, Fourier transform infrared (FT-IR) spectroscopic imaging to identify different components of cardiac tissue by their chemical and molecular basis aided by computer recognition, rather than by visual examination using optical microscopy. We studied this technique in assessment of cardiac transplant rejection to evaluate efficacy in an example of complex cardiovascular pathology. We recorded data from human cardiac transplant patients' biopsies, used a Bayesian classification protocol and developed a visualization scheme to observe chemical differences without the need of stains or human supervision. Using receiver operating characteristic curves, we observed probabilities of detection greater than 95% for four out of five histological classes at 10% probability of false alarm at the cellular level while correctly identifying samples with the hallmarks of the immune response in all cases. The efficacy of manual examination can be significantly increased by observing the inherent biochemical changes in tissues, which enables us to achieve greater diagnostic confidence in an automated, label-free manner. We developed a computational pathology system that gives high contrast images and seems superior to traditional staining procedures. This study is a prelude to the development of real time in situ imaging systems, which can assist interventionists and surgeons actively during procedures.

Regulating Biocompatibility of Carbon Spheres via Defined Nanoscale Chemistry and a Careful Selection of Surface Functionalities.

A plethora of nanoarchitectures have been evaluated preclincially for applications in early detection and treatment of diseases at molecular and cellular levels resulted in limited success of their clinical translation. It is important to identify the factors that directly or indirectly affect their use in human. We bring a fundamental understanding of how to adjust the biocompatibility of carbon based spherical nanoparticles (CNPs) through defined chemistry and a vigilant choice of surface functionalities. CNPs of various size are designed by tweaking size (2-250 nm), surface chemistries (positive, or negatively charged), molecular chemistries (linear, dendritic, hyperbranched) and the molecular weight of the coating agents (MW 400-20 kDa). A combination of in vitro assays as tools were performed to determine the critical parameters that may trigger toxicity. Results indicated that hydrodynamic sizes are potentially not a risk factor for triggering cellular and systemic toxicity, whereas the presence of a highly positive surface charge and increasing molecular weight enhance the chance of inducing complement activation. Bare and carboxyl-terminated CNPs did present some toxicity at the cellular level which, however, is not comparable to those caused by positively charged CNPs. Similarly, negatively charged CNPs with hydroxyl and carboxylic functionalities did not cause any hemolysis.

Urocanic acid as an efficient hydroxyl radical scavenger: a quantum theoretical study.

The photoisomerization of urocanic acid (UCA)--which is present in human skin epidermis, where it acts as a sunscreen--from its trans isomer to its cis isomer upon exposure to UV-B radiation is known to cause immunosuppression. In recent years, the antioxidant properties of UCA (it acts as a hydroxyl radical scavenger) have also been recognized. In view of this, the mechanisms of stepwise reactions of trans-UCA with up to four hydroxyl radicals were investigated. The molecular geometries of the different species and complexes involved in the reactions (reactant, intermediate and product complexes, as well as transition states) were optimized via density functional theory in the gas phase. Solvation in aqueous media was treated with single point energy calculations using DFT and the polarizable continuum model. Single point energy calculations in the gas phase and aqueous media were also carried out using second-order Møller-Plesset perturbation theory (MP2). The AUG-cc-pVDZ basis set was employed in all calculations. Corrections for basis set superposition error (BSSE) were applied. Vibrational frequency analysis was performed for each optimized structure to ensure the validity of the optimized transition states. It was found that the binding of the first OH· radical to UCA involves a positive energy barrier, while subsequent reactions of OH· radicals are exergonic. Transition states were successfully located, even in those cases where the barrier energies were found to be negative. The cis-trans isomerization barrier energy of UCA and that of the first OH· radical addition to UCA are comparable, meaning that both processes can occur simultaneously. It was found that UCA could serve as an antioxidant in the form of an efficient OH· radical scavenger.

Quantum theoretical study of cleavage of the glycosidic bond of 2'-deoxyadenosine: base excision-repair mechanism of DNA by MutY.

The enzyme adenine DNA glycosylase, also called MutY, is known to catalyze base excision repair by removal of adenine from the abnormal 2'-deoxyadenosine:8-oxo-2'-deoxyguanosine pair in DNA. The active site of the enzyme was considered to consist of a glutamic acid residue along with two water molecules. The relevant reaction mechanism involving different barrier energies was studied theoretically. Molecular geometries of the various molecules and complexes involved in the reaction, e.g., the reactant, intermediate, and product complexes as well as transition states, were optimized employing density functional theory at the B3LYP/6-31G(d,p) level in the gas phase. It was followed by single-point energy calculations at the B3LYP/AUG-cc-pVDZ, BHandHLYP/AUG-cc-pVDZ, and MP2/AUG-cc-pVDZ levels in the gas phase. Single-point energy calculations were also carried out at the B3LYP/AUG-cc-pVDZ and BHandHLYP/AUG-cc-pVDZ levels in aqueous media as well as in the solvents chlorobenzene and dichloroethane. For the solvation calculations, the integral equation formalism of the polarizable continuum model (IEF-PCM) was employed. It is found that glutamic acid along with two water molecules would effectively cleave the glycosidic bond of adenosine by a new two-step reaction mechanism proposed here which is different from the three-step mechanism proposed by other authors earlier regarding the working mechanism of MutY.

A quantum chemical study of repair of O6-methylguanine to guanine by tyrosine: evaluation of the winged helix-turn-helix model.

The winged helix-turn-helix model for the repair of O6-MeG to guanine involving the reaction of O6-MeG with a tyrosine residue of the protein O6-alkylguanine-DNA alkyltransferase (AGT) was examined by studying the reaction mechanism and barrier energies. Molecular geometries of the species and complexes involved in the reaction, i.e. the reactant, intermediate and product complexes as well as transition states, were optimized employing density functional theory in gas phase. It was followed by single point energy calculations using density functional theory along with a higher basis set and second order M(phi)ller-Plesset perturbation theory (MP2) along with two different basis sets in gas phase and aqueous media. For the solvation calculations in aqueous media, the integral equation formalism of the polarizable continuum model (IEF-PCM) was employed. Vibrational frequency analysis was performed for each optimized structure and genuineness of transition states was ensured by visualizing the vibrational modes. It is found that tyrosine can repair O6-MeG to guanine by a two-step reaction. The present results have been compared with those obtained considering the helix-turn-helix model where the repair reaction primarily involves cysteine and occurs in a single-step. It is concluded that the repair through tyrosine envisaged in the winged helix-turn-helix model would be less efficient than that through cysteine envisaged in the helix-turn-helix model.

Vibrational spectra of cysteine zwitterion and mechanism of its formation: bulk and specific solvent effects and geometry optimization in aqueous media.

Vibrational spectrum of the zwitterionic (Z)-forms of cysteine has been studied considering full geometry optimization under the bulk solvent effect of aqueous media combined with the solvent effect of up to three specific water molecules. The tautomerization barrier energy of the molecule from its normal (N) to the Z form has also been obtained. Geometry optimization was performed at the B3LYP/AUG-cc-pVDZ level which was followed by single point energy calculations at the MP2/AUG-cc-pVDZ level of theory in both gas phase and aqueous media. Transition states (TS) were located between the N and Z-forms of cysteine complexed with one to three water molecules and also without any complexed water molecule. The bulk solvent effect of aqueous media was treated using the integral equation formalism of the polarizable continuum model (IEF-PCM). It has been found that the barrier energy decreases with the increasing number of complexed water molecules significantly. Two conformers (A, B) of Z-cysteine are found to have comparable stabilities. It is shown that agreement between the experimentally observed and our calculated vibrational frequencies for Z-cysteine, at the present level of treatment, is improved significantly for 22 out of 27 frequencies. For these 22 frequencies, for the more stable conformer (A) of Z-cysteine, the rms value of differences between our calculated and experimentally observed frequencies reduces from 22 to 11 cm(-1) in going from 0 to 3 complexed water molecules. Certain vibrational frequencies have been identified with the help of which the conformers A and B of Z cysteine can be identified.